Growing Sport Analytics

I have been thinking a great deal about transforming performance and leading ahead of the curve this year. By coincidence this year is ending with the screening of Moneyball.
I have been interested in particular in how secondary data can inform and support coaches and the coaching process.
One of the catalysts in my thinking has been Usama Fayyad. I heard Usama speak at a knowledge discovery in databases conference in Sydney in 2005. His 1996 paper From Data Mining to Knowledge Discovery in Databases co-written with Gregory Piatetsky-Shapiro and Padhraic Smyth was my first engagement with a domain of enquiry that has become a primary focus for me.
In their 1996 paper the authors point out that:

Across a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational theo- ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD).

I spent much of the 1980s and 90s collecting data about performance in rugby union and a number of other sports. All of these data were collected with hand notation systems. My interest then and now is the pattern of observable individual and team behaviour.
I saw an early copy of Michael Lewis’s book The Art of Winning an Unfair Game (2003) and was attracted intuitively to the power of sabermetrics. I saw in Bill James’s work the passion that fired Charles Reep in his observations of association football. Michael, Bill and Charles had a predecessor in Hugh Fullerton who in 1910 wrote about The Inside Game of baseball.
In his paper, Hugh Fullerton points out:

Last season (1909) I arranged with scorers to record hits of various kinds, and secured the scores thus kept on 40 Central League games, 26 American Association games, and fourteen college games to compare with major league scores kept in the same manner. In the college games one grounder in every 8 1/3 passed the infielders. In the Central League one in 10 7/12, in the American Association one in 12 2/43, and in the American National Leagues (45 games of my own scoring) one in every 15 3/16.

He adds that:

The figures were amazing, as they followed so closely the classification of the leagues. They proved that there is a reason for the “class”, but the proof is not found in the mathematics, but in two word (unless you hyphenate them), “team work.”

For Hugh Fullerton the inside game is “the art of getting the hits that “he couldn’t have got anyhow””.
102 years on from Hugh Fullerton’s research there is a growing community of practice in sport analytics. A recent example of this incandescence was a Sports Analytics conference in Manchester in November.
Speakers at the conference included: Bill Gerrard, Raffaele Poli, Simon Wilson, David Fallows, Gavin Fleig, Ed Sulley, Ian Lenagan, Rob Lowe, Fergus Connolly, Ian Graham, Nick Broad and Steve Houston.
Usama Fayyad, Gregory Piatetsky-Shapiro and Padhraic Smyth  point out that Knowledge KDD is “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data”.
I believe they have provided a fundamental guide to the family resemblances that characterise the analysis of sport performance:

Here, data are a set of facts (for example, cases in a database), and pattern is an expression in some language describing a subset of the data or a model applicable to the subset. Hence, in our usage here, extracting a pattern also designates fitting a model to data; finding structure from data; or, in general, making any high-level description of a set of data. The term process implies that KDD comprises many steps, which involve data preparation, search for patterns, knowledge evaluation, and refinement, all repeated in multiple iterations. By nontrivial, we mean that some search or inference is involved; that is, it is not a straightforward computation of predefined quantities like computing the average value of a set of numbers.

I am hopeful that 2012 will provide opportunities to share data throughout the community of practice that is sport analytics.
Photo Credits
Australian bowler, Bill O’Reilly, demonstrates his famous grip


  1. hmmmm facinating. Ive just become interested in learning analytics based on suddenly realising just how much data is generated by your average learning management system (like Moodle). Most institutions make no use of this – but it could be informing organisation performance in learner support and engagement.
    You have a wonderfully far ranging interests Keith. Thats not excluding good coffee and dojo bread!


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