|
on Sports and Economics |
| By: | Kappes, Heather Barry; Alter, Adam L; Edwards, Griffin S; Berri, David J |
| Abstract: | One major challenge facing policymakers is to design education and workplace training programs that are appropriately challenging. We review previous research which suggests that difficult training is better than easy training. However, surveys we conducted of students and of expert sport coaches showed that many prescribed easy rather than difficult training for those they coached. We analyzed the performance of NCAA basketball teams in postseason tournaments to see whether the existing research, largely on individuals in short-term situations, would generalize to teams in the long run. Indeed, playing difficult nonconference (training) games modestly improved performance for NCAA teams in the postseason. Difficult training particularly benefitted teams that lost many nonconference games, and the effect of difficulty was positive within the range of difficulty NCAA teams actually encounter, making it clear that difficult training is superior. We suggest that our results can be generalized beyond sports, although with careful consideration of differences between NCAA basketball teams and other teams that may limit generalizability. These results suggest that policymakers might consider amplifying the difficulty of team training exercises under certain conditions. |
| Keywords: | Learning; Training; Practice effects; Teams; Self-versus-others |
| JEL: | J50 |
| Date: | 2019–06–28 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:101087&r=all |
| By: | Grilli, Gianluca; Curtis, John; Hynes, Stephen; O'Reilly, Paul |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:esr:wpaper:rb201824&r=all |
| By: | Geis-Thöne, Wido |
| Abstract: | Männer, die im Alter von 25 bis 34 Jahren mindestens einmal wöchentlich Sport getrieben haben, können ihre Bruttostundenlöhnen in den folgenden neun Jahren um 6,39 Euro mehr steigern als sportlich Inaktive. Auch wenn die Wirkungszusammenhänge noch unklar sind, sollte darauf hingearbeitet werden, dass möglichst alle jungen Menschen sportlich aktiv werden. |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:iwkkur:362019&r=all |
| By: | Sam Ganzfried; Max Chiswick |
| Abstract: | Poker is a large complex game of imperfect information, which has been singled out as a major AI challenge problem. Recently there has been a series of breakthroughs culminating in agents that have successfully defeated the strongest human players in two-player no-limit Texas hold 'em. The strongest agents are based on algorithms for approximating Nash equilibrium strategies, which are stored in massive binary files and unintelligible to humans. A recent line of research has explored approaches for extrapolating knowledge from strong game-theoretic strategies that can be understood by humans. This would be useful when humans are the ultimate decision maker and allow humans to make better decisions from massive algorithmically-generated strategies. Using techniques from machine learning we have uncovered a new simple, fundamental rule of poker strategy that leads to a significant improvement in performance over the best prior rule and can also easily be applied by human players. |
| Date: | 2019–06 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1906.09895&r=all |