| Abstract:International Journal of Sport Finance, Volume 15, No.3, August 2020. |
| Transfer Policy and Football Club Performance: Evidence from Network Analysis Authors: Dennis Coates |
| Abstract:This study considers the football transfer market as a network and analyzes how characteristics of a football club’s player transfer network activities influence club performance. We use data on 23,220 unique football clubs from 189countries from 1996 through 2016. Our results show that for sport performance the best strategy is to have well-established relations with a limited number of partner clubs, especially in the domestic league. However, transfer policy focused on international deals improves financial performance of football clubs. These findings provide club management with insights on optimal transfer policy with regard to the balance between sport and financial performance. |
| FIFA World Cup: A Case of (In)efficiency of the Betting Market Authors: Ricardo Manuel Santos |
| Abstract:Using data from all FIFA World Cup competitions that took place between 1994 and 2014, a step logit model is estimated to forecast the likelihood of success of each team in each tournament. The model correctly identifies the winner in five out of the six tournaments, and among many variables considered, key contributors to the model’s forecasting performance are identified. Using only the information available by the date preceding each of the last two in-sample World Cups, we can perform a more ambitious test of the model’s ability to forecast the winner at future tournaments. Our results indicated that Spain would win in 2010 and Germany in 2014, as they did. Our results have strong implications about which information a sophisticated bettor should process when participating in the betting market. We show that, using bookmaker odds and model probabilities, a bettor could (consistently) make a profit. Therefore, our results hint at the possibility of deviations from efficiency in the large World Cup betting market. |
| Las Vegas Point Spread Values and Quantifying the Value of an NBA Player Authors: Adam Hoffer and Jared A. Pincin |
| Abstract:This paper uses Las Vegas Sportsbooks individual point spread values (PSVs) to estimate the first marginal product estimates of NBA players. Starting with the individual PSVs, we predict PSVs from performance statistics and use the predicted values to estimate a player’s marginal product. We then compare the NBA estimations with existing, better-established measures of player performance, including player efficiency rating, win shares, and value over replacement player. The results show that the estimates using statistical performance to predict PSVs are in-line with other estimates of player performance. We conclude that PSVs are a comparable measure of marginal product to existing NBA performance composites. |
| Strongest Team Favoritism in European National Football: Myth or Reality? Authors: Francesco Audrino |
| Abstract:Are the financially and institutionally strongest clubs capable of systematically reaching the top positions in the European national football leagues treated differently in terms of awarded sanctions because of the external off-the-pitch pressure they can put on match officials? This study helps shed some light on this controversial question fiercely debated among fans and sports journalists and extends our knowledge of how football match officials may be un-consciously influenced by external (social) forces. Except for France where the evidence is weak, data analysis of the top five European leagues for the seasons from 2011-2012 to 2017-2018 provides empirical evidence supporting the existence of referees’ off-the-pitch strongest team bias. In fact, in England, referees award significantly more yellow cards and total booking points (an aggregate measure of yellow and red cards) to the opponents’ players, and in Italy, Germany, and Spain, significantly fewer yellow cards and total booking points are given to the top teams’ players. The referees’ strongest team bias comes on top of the referees’ home bias, as discussed in the previous literature, and displays a non- eligible size that can reach approximately the same size of the referees’ home bias in some cases. |
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