Some Champions Trophy 2017 Performance Profiles


The 2017 Champions Trophy was won by Pakistan in a final against India on 18 June at The Oval.

During the tournament, there were nine games completed without reference to Duckworth-Lewis-Stern scores. My record of these nine games is:

From these data, my median profiles of winners and losers were:

I was interested to look at the performance of Pakistan and India against these profiles to reflect on where the final might have been won or lost.



Photo Credit

The Kia Oval (Gareth Williams, CC BY 2.0)

A graphical display of a football game played in Delft


Jurryt van de Vooren has unearthed a notation from a game of football played between Delfia Hollandia Combinatie (DHC) and Goudse Sportvereniging (GSV). The record indicates that this was a promotion game (promotie-wedstrijd).

The Delpher newspaper archive has a digital copy of the notation. This was published on 2 May 1932 in the Delftsche Courant. There is a match report too (Een narrow escape) for the game played on 1 May 1932.

In Dutch, the title of the notation is: De verplaasting van den bal is grafisch weergegeven in de lengterichting van het veld.

I wondered if this might be the appropriate translation: The ball displacement is graphically displayed in the longitudinal direction of the field.

The Notation

The displacement is notated with the help of nine symbols.

First half

Second half

The notation has a time reference for each possession in the game. The time is set in blocks of five minutes with single minutes marked within each five-minute block.

I wondered if the accuracy of timing used some of the chronographs available at that time. This is one from Longines in 1929:

I wondered too where the analyst sat during the game. There appeared to be a big crowd there.

Match Report

Both teams are listed in the report in their 2-3-5 formations. One of the DHC players, Joop van Nellen (1910-1992) played for the Netherlands in twenty-seven international fixtures. He made his debut in December 1928, aged 18, and played his last game (against Belgium) in February 1937. He won his first twelve caps while playing for Delft at the second level of Dutch football.

Gouda won the toss and chose to play with the sun and wind at their backs.

Gouda scored first in the 13th minute. It took forty minutes for Delft to equalise. One minute later Delft took the lead. A goal in the 89th minute gave Delft a 3v1 win.

Pattern of Play

Delft were the home team and appeared to control the game for large parts of the notation.

Gouda’s goal looks like a very efficient counterattack:

After losing the lead in the second half, Gouda have an intense five minutes working to get back into the game (and have one shot in this time):

Delft control the final quarter of the game. Their goalkeeper is involved only once in this time. The game ends with Gouda on the attack after conceding a third goal.

A Case Study

I think this notation, one of the earliest in association football, would make for a fascinating discussion in performance analysis classes that spend some time considering real-time and lapsed-time hand notation.

There is sufficient detail for us to construct a narrative of the game.

It would be a great project to annotate a present-day game in the same way. There is, for example, just one formally noted stoppage in play in the entire game (14th minute of the first half). Time added on by the referee is 2 minutes in the first half and approximately 90 seconds in the second half. What has changed in the game in nine decades?

Photo Credits

DHC in action (Delftsche Courant, 2 May 1932)

Notations (Delftsche Courant, 2 May 1932)



It has been a feast of delight this week on Clyde Street.

I have been following up on the ideas shared by David Zeevi and his colleagues about personalisation and prediction. One part of the paper has stayed with me:

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 have been thinking about the implications of this for the learning and performance environments we build and maintain in sport. A week of investigating research and practice in precision medicine has encouraged me to contemplate the skills we might need to be engaged with long-term athlete and coach personal flourishing.

Leroy Hood and his colleagues have championed a systems biology approach to predictive, preventative, personalised and participatory healthcare. They noted:

Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system to assess their interactions and to integrate that information into graphical network models that serve as predictive hypotheses to explain emergent behaviours. (2004:640)

Leroy and Andrea Watson predicted:

a paradigm shift in medicine will take place within the next two decades replacing the current approach, which is predominantly reactive, to one that can increasingly predict and prevent cellular dysfunction and disease. (2004:179)

A decade later, researchers and practitioners are embedded in precision medicine and deliver treatments:

targeted to the needs of individual patients on the basis of genetic, biomarker, phenotypic, or psychosocial characteristics that distinguish a given patient from other patients with similar clinical presentations. (Larry Jameson and Dan Longo, 2015)

This shift requires a fundamental rethink of how to deliver personal care. Reza Mirnezami, Jeremy Nicholson and Ara Darzi (2012), for example observe:

Precision medicine will require handling of multi-parametric data and some proficiency in interpreting “-omics” data, placing new demands on medical professionals, who may be ill equipped to deal with the anticipated complexity and volume of new information. Addressing these challenges will require effective clinical decision support tools and new educational models.

These new educational models fascinate me, particularly in the context of understanding how analytics are embedded in coaches’ learning pathways in formal accreditation and in continuing learning.

Two posts this week added to my reflections about precision.

In the first post, Ricardo Tavares considered Why We Need Positional Data in conversations about football analysis. I really enjoyed the way Ricardo shared the process of analysis using a single example. I delight in n=1 studies and their resonance with other performances.

What made Ricardo’s post of particular interest to me was the open sharing he demonstrated. His post concludes:

You can download the the csv file with the data here (x and y coordinates on a scale of 0 to 100). The player data is here (player numbers and names aren’t filled yet, but they should be up soon).

If you know Python, you can also view (and download) the Jupyter Notebook that made the animations here (or here, for a more browser friendly version).

The second post, Protecting an NHL Player’s Greatest Asset is an interview with the San Jose Sharks’ trainer, Mike Potenza. In the interview, Mike notes:

The NHL is one of the longest seasons in professional sports. Each team will commonly play 13-16 games per month with no consistency to the format of days they play. West coast teams will travel more than the east coast teams due to the proximity of franchise locations. Given the compressed game schedule, travel schedule and requirements for mandatory days off per league rules, practice time is limited but still a valuable commodity.

In San Jose our goal is to monitor every practice during the season, which includes pre-season training camp. Team workload/intensity and duration of monitored practice times are shared with the coaching staff so they have useful information when planning the next workday and the yearly work to rest schedule.

Mike uses these data to monitor

  • Accumulative work load
  • Training effect
  • % of max HR
  • High intensity duration

Mike discussed the absence of HR monitoring in games (an NHL stipulation):

It is a major missing piece of the puzzle that we do not have game data from HR monitors or GPS units because we do not know the cost of a NHL hockey game and the stresses that go along with that. The frequency and physical component of games per week is very high both in the regular season and in the playoffs. This being the case, missing game data forces performance coaches to only draw conclusions from sub-maximal practice data that only can be compared to practice and not the main show! To further dissect the issue, by not monitoring games, performance coaches do not have a reference for the metabolic specific zones achieved in games. These are extremely useful pieces of data that would be used to assign HR training zones for players who earn substantially different time on ice (T.O.I.) accumulations.

This brought me back to think about the precision we bring to the observation about each player’s performance and the decision-support we use to modulate training, whilst having some clarity about long-term flourishing.

These are active debates in healthcare and I see a similar need to have these conversations in sport.

Ultimately, precision medicine should ensure that patients get the right treatment at the right dose at the right time, with minimum ill consequences and maximum efficacy. (Reza Mirnezami, Jeremy Nicholson and Ara Darzi, (2012)

Ricardo’s use of a single event to raise fundamental questions about what we observe and analyse in sport took me back to Ference Marton (1994:7) and the idea of phenomenography that aims “at a very specific level of description, corresponding to a level of experience believed to be critical as far as our capabilities for experiencing certain phenomena in certain ways are concerned”.

Considerations about precision in sport contexts require us, I believe, to make sense of digital records of performance in our everyday practice. As in medicine, we have immense opportunities to explore new paradigms. Phenomenography encourages us to reflect on “the question of people being capable, or not, of experiencing and acting in certain ways” (Ference Marton (1994:7).

This seems to me to be the start of an agenda to discuss our educational models.

Photo Credit

Dawn on Clyde Street (Keith Lyons, CC BY 4.0)

Real-time monitoring (Firstbeat)