The Sport League's Dilemma: Competitive Balance versus Incentives to Win

We analyze a dynamic model of strategic interaction between a professional sport league that organizes a tournament, the teams competing to win it, and the broadcasters paying for the rights to televise it.Teams and broadcasters maximize expected profits, while the league's objective may be either to maximize the demand for the sport or to maximize the teams'joint profits.Demand depends positively on symmetry among teams (competitive balance) and how aggressively teams try to win (incentives to win).Revenue sharing increases competitive balance but decreases incentives to win.Under demand maximization, a performance-based reward scheme (used by European sport leagues) may be optimal. Under joint profit maximization, full revenue sharing (used by many US leagues) is always optimal.These results reflect institutional differences among European and American sports leagues.sport;competition;incentives;broadcasting industry;revenue sharing

Revenue Sharing and Competitive Balance in a Dynamic Contest Model

This paper presents a dynamic model of talent investments in a team sports league with an infinite time horizon. We show that the clubs' investment decisions and the effects of revenue sharing on competitive balance depend on the following three factors: (i) the cost function of talent investments, (ii) the clubs' market sizes, and (iii) the initial endowments of talent stock. We analyze how these factors interact in the transition to the steady state as well as in the steady state itself.Contest, Sports Economics, Competitive Balance, Revenue Sharing

Digital supply chain through dynamic inventory and smart contracts

This paper develops a digital supply chain game, modeling marketing and operation interactions between members. The main novelty of the paper concerns a comparison between static and dynamic solutions of the supply chain game achieved when moving from traditional to digital platforms. Therefore, this study proposes centralized and decentralized versions of the game, comparing their solutions under static and dynamic settings. Moreover, it investigates the decentralized supply chain by evaluating two smart contracts: Revenue sharing and wholesale price contracts. In both cases, the firms use an artificial intelligence system to determine the optimal contract parameters. Numerical and qualitative analyses are used for comparing configurations (centralized, decentralized), settings (static, dynamic), and contract schemes (revenue sharing contract, wholesale price contract). The findings identify the conditions under which smart revenue sharing mechanisms are worth applying

The Sport League's Dilemma: Competitive Balance versus Incentives to Win

We analyze a dynamic model of strategic interaction between a professional sport league that organizes a tournament, the teams competing to win it, and the broadcasters paying for the rights to televise it. Teams and broadcasters maximize expected profits, while the league's objective may be either to maximize the demand for the sport or to maximize the teams' joint profits. Demand depends positively on symmetry among teams (competitive balance) and how aggressively teams try to win (incentives to win). Revenue sharing increases competitive balance but decreases incentives to win. Under demand maximization, a performance-based reward scheme (used by European sport leagues) may be optimal. Under joint profit maximization, full revenue sharing (used by many US leagues) is always optimal. These results reflect institutional differences among European and American sports leagues.

Competitive Balance vs. Incentives to Win: A Theoretical Analysis of Revenue Sharing

We analyze a dynamic model of strategic interaction between the league organizing a professional sport, the teams playing the tournament organized by this league, and broadcasters competing for the rights to televise their matches. Teams and broadcasters maximize expected profits, while the league's objective may be either to maximize the demand for the sport or to maximize the teams' joint profits. Demand depends positively on competitive balance among teams and how intensively they compete to win the tournament. Revenue sharing increases competitive balance but decreases incentives to win. Under demand maximization, a performance-based reward scheme (as used by European top soccer leagues for national TV deals) may be optimal. Under joint profit maximization, full revenue sharing (as used by US team sport leagues for national TV deals) is always optimal.

Sharing of Unlicensed Spectrum by Strategic Operators

Facing the challenge of meeting ever-increasing demand for wireless data, the industry is striving to exploit large swaths of spectrum which anyone can use for free without having to obtain a license. Major standards bodies are currently considering a proposal to retool and deploy Long Term Evolution (LTE) technologies in unlicensed bands below 6 GHz. This paper studies the fundamental questions of whether and how the unlicensed spectrum can be shared by intrinsically strategic operators without suffering from the tragedy of the commons. A class of general utility functions is considered. The spectrum sharing problem is formulated as a repeated game over a sequence of time slots. It is first shown that a simple static sharing scheme allows a given set of operators to reach a subgame perfect Nash equilibrium for mutually beneficial sharing. The question of how many operators will choose to enter the market is also addressed by studying an entry game. A sharing scheme which allows dynamic spectrum borrowing and lending between operators is then proposed to address time-varying traffic and proved to achieve perfect Bayesian equilibrium. Numerical results show that the proposed dynamic sharing scheme outperforms static sharing, which in turn achieves much higher revenue than uncoordinated full-spectrum sharing. Implications of the results to the standardization and deployment of LTE in unlicensed bands (LTE-U) are also discussed.Comment: To appear in the IEEE Journal on Selected Areas in Communications, Special Issue on Game Theory for Network