The 'nurdle to leg' and other ways of winning cricket matches

The emergence of Twenty20 cricket at the elite level has been marketed on the excitement of the big hitter, where it seems that winning is a result of the muscular batter hitting boundaries at will. This version of the game has captured the imagination of many young players who all want to score runs with “big hits”. However, in junior cricket, boundary hitting is often more difficult due to size limitations of children and games played on outfields where the ball does not travel quickly. As a result, winning is often achieved via a less spectacular route – by scoring more singles than your opponents. However, most standard coaching texts only describe how to play boundary scoring shots (e.g. the drives, pulls, cuts and sweeps) and defensive shots to protect the wicket. Learning to bat appears to have been reduced to extremes of force production, i.e. maximal force production to hit boundaries or minimal force production to stop the ball from hitting the wicket. Initially, this is not a problem because the typical innings of a young player (<12 years) would be based on the concept of “block” or “bash” – they “block” the good balls and “bash” the short balls. This approach works because there are many opportunities to hit boundaries off the numerous inaccurate deliveries of novice bowlers. Most runs are scored behind the wicket by using the pace of the bowler’s delivery to re-direct the ball, because the intrinsic dynamics (i.e. lack of strength) of most children means that they can only create sufficient power by playing shots where the whole body can contribute to force production. This method works well until the novice player comes up against more accurate bowling when they find they have no way of scoring runs. Once batters begin to face “good” bowlers, batters have to learn to score runs via singles. In cricket coaching manuals (e.g. ECB, n.d), running between the wickets is treated as a separate task to batting, and the “basics” of running, such as how to “back- up”, carry the bat, calling and turning and sliding the bat into the crease are “drilled” into players. This task decomposition strategy focussing on techniques is a common approach to skill acquisition in many highly traditional sports, typified in cricket by activities where players hit balls off tees and receive “throw-downs” from coaches. However, the relative usefulness of these approaches in the acquisition of sporting skills is increasingly being questioned (Pinder, Renshaw & Davids, 2009). We will discuss why this is the case in the next section.

The changing face of practice for developing perception : action skill in cricket

The use of bowling machines is common practice in cricket. In an ideal world all batters would face real bowlers in practice sessions, but this is not always possible, for many reasons. The clear advantage of using bowling machines is that they alleviate the workload required from bowlers (Dennis, Finch & Farhart, 2005) and provide relatively consistent and accurate ball delivery which may not be otherwise available to many young batters. Anecdotal evidence suggests that many, if not most of the world’s greatest players use these methods within their training schedules. For example, Australian internationals, Michael Hussey and Matthew Hayden extensively used bowling machines (Hussey & Sygall, 2007). Bowling machines enable batsmen to practice for long periods, developing their endurance and concentration. However, despite these obvious benefits, in recent times the use of bowling machines has been questioned by sport scientists, coaches, ex- players and commentators. For example, Hussey’s batting coach comments “…we never went near a bowling machine in [Michael’s] first couple of years, I think there’s something to that …” (Hussey & Sygall, 2007, p. 119). This chapter will discuss the efficacy of using bowling machines with reference to research findings, before reporting new evidence that provides support for an alternative, innovative and possibly more representative practice design. Finally, the chapter will provide advice for coaches on the implications of this research, including a case study approach to demonstrate the practical use of such a design.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.