Star-factors of tournaments

Let S_m denote the m-vertex simple digraph formed by m-1 edges with a common tail. Let f(m) denote the minimum n such that every n-vertex tournament has a spanning subgraph consisting of n/m disjoint copies of S_m. We prove that m lg m - m lg lg m <= f(m) <= 4m^2 - 6m for sufficiently large m.Comment: 5 pages, 1 figur

Fund family tournament and performance consequences: evidence from the UK fund industry

By applying tournament analysis to the UK Unit Trusts data, the results support significant risk shifting in the family tournament; i.e. interim winning managers tend to increase their level of risk exposure more than losing managers. It also shows that the risk-adjusted returns of the winners outperform those of the losers following the risk taking, which implies that risk altering can be regarded as an indication of managers’ superior ability. However, the tournament behaviour can still be a costly strategy for investors, since winners can be seen to beat losers in the observed returns due to the deterioration in the performance of their major portfolio holdings

FIFA World Cup: Factors that explain the performances of National Football Teams

This paper examines the determinants of FIFA World Cup performances of nations. The study incorporates socioeconomic, cultural, demographic and football-specific factors to investigate how World Cup results can be explained. A linear regression is used to study the last five tournaments, and the model finds that being seeded for the draw, and the host country effect are statistically significant variables. Additionally, I discover two new variables – namely, having a star player and having become a member of FIFA before 1924, as being statistically significant in my analysis

Optimaztion of Fantasy Basketball Lineups via Machine Learning

Machine learning is providing a way to glean never before known insights from the data that gets recorded every day. This paper examines the application of machine learning to the novel field of Daily Fantasy Basketball. The particularities of the fantasy basketball ruleset and playstyle are discussed, and then the results of a data science case study are reviewed. The data set consists of player performance statistics as well as Fantasy Points, implied team total, DvP, and player status. The end goal is to evaluate how accurately the computer can predict a player’s fantasy performance based off a chosen feature set, selection algorithm, and probabilistic methods