Anticipating the Crusaders v British and Irish Lions Game 2017 (#cruvbil)

I have been following some of the secondary data available from the 2017 Super Rugby competition.

The Crusaders have been a dominant force in this season’s competition so I thought I would look at their data in advance of their upcoming game against the British Lions.

For all Super Rugby games up to the break for international fixtures (15 rounds), I have these data:

My data for the Crusaders are:

It should be a great game on a dry evening in Christchurch. It will be fascinating to see how the British and Irish Lions deal with the Crusaders’ try scoring threat and how the Crusaders sustain their second half defence against a Lions team determined to gain some momentum after their defeat against the Blues.

The game will be refereed by Mathieu Raynal (France).

Photo Credit

Crusaders 25 vs. Hurricanes 17 ( Geof Wilson, CC BY-NC-ND 2.0)


The British and Irish Lions won the game 12 points to 3. No tries were scored in the game.

Gut reaction

I came across a 2015 paper written by David Zeevi and his colleagues this week.

The paper is titled Personalized Nutrition by Prediction of Glycemic Responses. Their graphical abstract is:

I had a profound, positive gut reaction to a paper about the gut. I think there is a lot to admire in this paper and I believe it has an important role to play in how we might present our research in sport as we explore how we might use analytics to transform performance.

Their brief abstract is:

People eating identical meals present high variability in post-meal blood glucose response. Personalized diets created with the help of an accurate predictor of blood glucose response that integrates parameters such as dietary habits, physical activity, and gut microbiota may successfully lower post-meal blood glucose and its long-term metabolic consequences.

I was particularly interested in how the research team analysed the data from their participant cohort:

We asked whether clinical and microbiome factors could be integrated into an algorithm that predicts individualized postprandial (post-meal) glycemic responses (PPGRs). To this end, we employed a two-phase approach. In the first, discovery phase, the algorithm was developed on the main cohort of 800 participants, and performance was evaluated using a standard leave-one-out cross validation scheme, whereby PPGRs of each participant were predicted using a model trained on the data of all other participants. In the second, validation phase, an independent cohort of 100 participants was recruited and profiled, and their PPGRs were predicted using the model trained only on the main cohort.

In their discussion, David and his colleagues note:

In this work we measured 46,898 PPGRs to meals in a population-based cohort of 800 participants. We demonstrate that PPGRs are highly variable across individuals even when they consume the same standardized meals. We further show that an algorithm that integrates clinical and microbiome features can accurately predict personalized PPGRs to complex, real-life meals even in a second independently collected validation cohort of 100 participants. Finally, personalized dietary interventions based on this algorithm induced lower PPGRs and were accompanied by consistent gut microbiota alterations.

The conclusion to the paper includes the following observation:

Dietary interventions based on our predictor showed significant improvements in multiple aspects of glucose metabolism, including lower PPGRs and lower fluctuations in blood glucose levels within a short 1-week intervention period. It will be interesting to evaluate the utility of such personalized intervention over prolonged periods of several months and even years. (My emphasis)

I do think this is an exemplary paper. I found the language and transparency of their narrative very engaging. There were some powerful visualisations to add to my engagement. Their use of machine learning has sent me off thinking about the generalisability of their approach.

Above all, I was left thinking about “the utility of such personalized intervention over prolonged periods of several months and even years” in a wider sense of personal learning and performance journeys. David and his colleagues have shone a microbiota light on how I might do this.

On the radar?

I missed the Twitter exchange about radar plots yesterday.

I caught up with some of the exchanges through a Ted Knutson post about Revisiting Radars.

Ted noted “The fact that lots of people have reactions to this type of work is a good thing, not a bad one”.

I have been using radar plots in my work for some time. I am mindful of the issues that Luke and Sam (@stat-sam) raise.

Some time ago (2008), I was fascinated by the title of a Purna Duggirala (Chandoo) post You are NOT spider man, so why do you use radar charts? I enjoyed Graham Odd’s (2011) critique of radar charts too.

Primed with Graham’s observation:

… the overall shape presented for a series on a radar chart does not leverage any of the pre-attentive attributes we perceive quantitatively. In essence, this means we are unable to attribute much genuine meaning to the shape of a series. The only patterns our visual perception can really discern in a data set presented as a radar chart are similarity and extreme outliers.

I tried to use my radar charts as a stimulus for conversation. Like, Ted, over the years I have found radar charts a good way to hook attention and trigger conversation. I had similar experiences to Ted in a variety of sport contexts:

In situations like this, visuals go a long way toward opening the conversation. If you show a table of numbers to a coach who isn’t already on board, you’re dead. Bar charts? Only mostly dead. Radars? Interesting… Tell me more.

My use of the charts acknowledged the limitations of the visualisation. Once the coaches had started to discuss the issues raised by a fallible (flawed) visualisation, we inevitably started to discuss how performance might be re-presented (represented) which led in many cases to some fascinating second-order conversations about observation and the narratives we build around performance … and other forms of visualisation.

My willingness to use radar charts dates back to William Anderson’s (1971) discussion of descriptive-analytic research in physical education. He notes:

Their principal concern is to collect accurate descriptive records of events in actual classrooms and to analyze these records in a way that enables a better understanding of the events. (1971:2)

He adds:

The descriptive records of teaching which emerge are in many ways like the descriptive record of a basketball game contained in a basketball shooting chart. The shooting chart is a diagram of a basketball court on which is recorded the number of each player who took a shot, the place on the court from which the shot was taken, and whether the shot was made. The shooting chart is a record of a critical dimension of “real world events” (the game). A careful examination of the chart can lead to understandings and insights which were not possible during the game itself. In much the same way, descriptive records of teaching provide a picture of real world events (classroom interaction) which lead to a deeper understanding of the teaching process. (1971:3)

Ted makes the point “As I designed them, radars exist to help you open the door with statistical novices, and from that perspective they have been wildly successful”.

This happened in conversations about pedagogy too in William Anderson’s work.

Like Ted, I am acutely aware of the flaws in radar charts. We have unprecedented expertise in sport now. The stories we can produce have immense visualisation resources to share performances. The key for me will be how we work with a variety of audiences in sport, and particularly in decision support for coaches, to achieve the level of engagement Ted reports:

Radars start a conversation. They get a reaction. And for whatever reason, football people are often more comfortable talking about and digesting them than almost any other vis type I have encountered. (Original emphasis.)

These issues are why I have included Audiences and Messages in my open, online discussion of sport informatics and analytics. We have great opportunities for conversation in our community of practice about diverse practices.

Photo Credit

Crystal web (Wendy, CC BY NC-ND 2.0)