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on Sports and Economics |
| By: | Deepak Srivastav (Indian Institute of Management Kozhikode); Puram Praveen (Indian Institute of Management Kozhikode); Rudra Sensarma (Indian Institute of Management Kozhikode); Anand Gurumurthy (Indian Institute of Management Kozhikode) |
| Abstract: | This study examines the relationship between salary dispersion and team performance in cricket, using the Indian Premier League (IPL) data from 2008 – 2019. We employ a dynamic panel regression to test the applicability of the Equity theory and the Tournament theory in explaining team performance. Our results show that higher salary dispersion positively affects team performance, supporting the tournament theory. The study also highlights the effect of relative and overall spending by teams on their performance. The findings could be used for managing players and teams better apart from fine-tuning the strategies during player bidding. This study contributes to the sports management literature by being among the first studies to explore the impact of salary dispersion on team performance in Twenty20 cricket. |
| Keywords: | Tournament theory, Equity theory, Twenty20 cricket, Indian Premier League, Dynamic panel estimation |
| Date: | 2021–03 |
| URL: | http://d.repec.org/n?u=RePEc:iik:wpaper:441&r= |
| By: | Ho Fai Chan; David A. Savage; Benno Torgler |
| Abstract: | Sporting events can be seen as controlled, real-world, miniature laboratory environments, approaching the idea of holding other things equal when exploring the implications of decisions, incentives, and constraints in a competitive setting (Goff and To llison 1990, Torgler 2009). Thus, a growing number of studies have used sports data to study decision making questions that have guided behavioural economics literature. Creative application of sports data can offer insights into behavioural aspects with implications beyond just sports. In this chapter, we will discuss the methodological advantages of seeing sport as a behavioural econom ics lab, concentrating on the settings, concepts, biases, and challenging areas. Beyond that, we will discuss que stions that have not yet been analysed, offering ideas for future studies using sports data. We will fu rther reflect on how AI has evolved; focusing, for example, on chess, which provides insights into the mechanism and machinery of decision-making. |
| Date: | 2021–05 |
| URL: | http://d.repec.org/n?u=RePEc:cra:wpaper:2021-20&r= |