|
on Sports and Economics |
| By: | Rubin, Allen |
| Abstract: | Win probabilities have become a staple on scoreboards in physical sports such as baseball and basketball. Esports, or competitive video games with sponsored teams and major audiences, typically lack this detailed statistical analysis, beyond bare-bones metrics and commentator intuition. However, the advantage of esports in their tendency to have a central record of every game event makes them ripe for statistical analysis through machine learning. Previous research has covered popular video game genres such as MOBAs, and has found success in predicting game winners most of the time. Counterstrike: Global Offensive (CSGO) is an esport that is unique in its round and game-based nature, allowing researchers to examine how short and long-term decisions can interplay in competitive environments. We introduce a dataset of CSGO games To assess factors such as player purchasing decisions and individual scores, we introduce 3 round and game win probability models. Finally, we evaluate the performances of the models. We successfully predict winners in the majority of cases, better than the map average baseline win statistics. |
| Date: | 2022–01–12 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:u9j5g&r= |
| By: | Uwe Sunde; Dainis Zegners; Anthony Strittmatter |
| Abstract: | This paper presents an empirical investigation of the relation between decision speed and decision quality for a real-world setting of cognitively-demanding decisions in which the timing of decisions is endogenous: professional chess. Move-by-move data provide exceptionally detailed and precise information about decision times and decision quality, based on a comparison of actual decisions to a computational benchmark of best moves constructed using the artificial intelligence of a chess engine. The results reveal that faster decisions are associated with better performance. The findings are consistent with the predictions of procedural decision models like drift-diffusion-models in which decision makers sequentially acquire information about decision alternatives with uncertain valuations. |
| Date: | 2022–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2201.10808&r= |
| By: | Thitithep Sitthiyot; Kanyarat Holasut |
| Abstract: | Concern about income inequality has become prominent in public discourse around the world. However, studies in behavioral economics and psychology have consistently shown that people prefer not equal but fair income distributions. Thus, finding a benchmark that could be used to measure fair income distribution across countries is a theoretical and practical challenge. Here a method for benchmarking fair income distribution is introduced. The benchmark is constructed based on the concepts of procedural justice, distributive justice, and authority's power in professional sports where it is widely agreed as an international norm that the allocations of athlete's salary are outcomes of fair rules, individual and/or team performance, and luck in line with no-envy principle of fair allocation. Using the World Bank data, this study demonstrates how the benchmark could be used to quantitatively gauge whether, for a given value of the Gini index, the income shares by quintile of a country are the fair shares or not, and if not, what fair income shares by quintile of that country should be. Knowing this could be useful for those involved in setting targets for the Gini index and the fair income shares that are appropriate for the context of each country before formulating policies toward achieving the Sustainable Development Goal 10 and other SDGs. |
| Date: | 2022–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2202.00917&r= |