Edited by John Goddard and Peter Sloane
Econometric modelling in sport is an area of quantitative research that has seen a significant amount of growth in recent years. An important factor that has contributed to the increased interest is no doubt the betting market: punters are striving to beat the market, whilst bookmakers are trying to stay ahead of a new breed of sophisticated professional gambler adept at using complex mathematical models to gain an advantage over the rest. Further, the advent of in-play betting has meant that new types of models are being developed and applied to a multitude of sports. This new wave of in-play modelling is facilitated by a massive increase in data availability. Football, a traditionally difficult and awkward sport to collate in-play information for, now has companies like Opta Sports and Prozone collecting data on every event in a match, and in the case of Prozone, recording the coordinates of all 22 players as they move around the pitch. The heightened interest in modelling of course coincides with ever-improving computing power, which itself acted as a catalyst for the emergence of the in-play betting market, in the form of high-speed internet connections. But the betting industry is not the only driving force behind a surge in interest in sports econometrics. Sports teams themselves are increasingly using quantitative analysis to gain an edge over their competitors. The best example of this is Major League Baseball in the US.
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