Sport Informatics and Analytics/Pattern Recognition/Knowledge Discovery/Sport examples

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Sport examples

We present two examples here for your consideration.

Chris Anderson and David Sally discuss the potential of an analytics approach to association football in their study of The Numbers Game[1].

In the introduction to their book, they write:

The clue to analytics is in the name. To make (those) numbers mean something, to learn something from them, they must be analysed. The key, for those at the vanguard of what some have called a data 'revolution and what we think of as football's reformation, is to work out what they need to be counting, and to discover why, exactly, what they are counting counts.[2]

Their book explores the analytics process and raises important empirical and methodological issues for this unit.

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A Numbers Game?

Read the introductory chapter in The Numbers Game, Football for Sceptics - The Counter)s) Reformation . Does their suggestion resonate with your experience of sport?

A storm is gathering in football. It is one that will wash away old certainties and change the game we know and love. It will be a game we view more analytically, more scientifically, where we do not accept what we have always been taught, but where we always ask why. The game will look the same, but the way we think about it will be almost unrecognizable[3].



The second example presented here is the paper written in 1997 by Inderpal Bhandari and his colleagues at the IBM TJ Watson Research Center. The paper is titled Advanced Scout: Data Mining and Knowledge Discovery in NBA Data. In the paper, they report their analysis of data gathered by a software program, Advanced Scout, that "seeks out and discovers interesting patterns in game data"[4]. We have chosen this paper to connect with the spirit of the literature of the time. The editor of the journal within which the paper was accepted was Gregory Piatetsky-Shapiro.

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Another coach on the team?

Read the paper here.

  • Can you see any parallels with the generic discussion in Usama Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth's paper[5]?
  • Mindful of your consideration of the Audiences and Messages theme of this unit, how might you go about sharing insights from the data you have gathered to offer the team another coach as suggested by Bob Salmi in the paper?



References

  1. ↑ Anderson, Chris; Sally, David (2013). The numbers game: why everything you know about football is wrong. London: Penguin.
  2. ↑ Anderson, Chris; Sally, David (2013). The numbers game: why everything you know about football is wrong. London: Penguin. pp. np.
  3. ↑ Anderson, Chris; Sally, David (2013). The numbers game: why everything you know about football is wrong. London: Penguin. pp. np.
  4. ↑ Bhandari, Inderpal et al. (1997). [http://www.cse.unr.edu/~sushil/class/ml/papers/local/nba.pdf "Advanced Scout: Data Mining and Knowledge Discovery in NBA Data"]. Data Mining and Knowledge Discovery 1 (1): 121. http://www.cse.unr.edu/~sushil/class/ml/papers/local/nba.pdf.
  5. ↑ Fayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases". AI Magazine 17 (3): 37-54. http://www.aaai.org/ojs/index.php/aimagazine/article/download/1230/1131/.
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