Volume 13, Issue 1

Each year the members of the Baseball Writers Association of America (BBWAA) vote for eligible former players to be inducted into the Baseball Hall of Fame. The BBWAA tabulates and releases vote totals, but individual ballots remain private. However, many voters forgo their ballot privacy to publish their ballots through various media channels. These publicly available ballots can be aggregated to create a subset of the true ballots. Using these released ballots and the totals released by the BBWAA, this research assesses what can be learned about the group of voters who chose to not disclose their ballot. Attributes of the known and unknown ballot groups are studied by looking at differences in voting preference for individual players as well as voting differences between classes of voters that are defined using latent class analysis (LCA).

Identifying the decisive matches in international football tournaments is of great relevance for a variety of decision makers such as organizers, team coaches and/or media managers. This paper addresses this issue by analyzing the role of the statistical approach used to estimate the outcome of the game on the identification of decisive matches on international tournaments for national football teams. We extend the measure of decisiveness proposed by Geenens (2014) in order to allow us to predict or evaluate the decisive matches before, during and after a particular game on the tournament. Using information from the 2014 FIFA World Cup, our results suggest that Poisson and kernel regressions significantly outperform the forecasts of ordered probit models. Moreover, we find that although the identification of the most decisive matches is independent of the model considered, the identification of other key matches is model dependent. We also apply this methodology to identify the favorite teams and to predict the most decisive matches in 2015 Copa America before the start of the competition. Furthermore, we compare our forecast approach with respect to the original measure during the knockout stage.

Cricketing knowledge tells us batting is more difficult early in a player’s innings but becomes easier as a player familiarizes themselves with the conditions. In this paper, we develop a Bayesian survival analysis method to predict the Test Match batting abilities for international cricketers. The model is applied in two stages, firstly to individual players, allowing us to quantify players’ initial and equilibrium batting abilities, and the rate of transition between the two. This is followed by implementing the model using a hierarchical structure, providing us with more general inference concerning a selected group of opening batsmen from New Zealand. The results indicate most players begin their innings playing with between only a quarter and half of their potential batting ability. Using the hierarchical structure we are able to make predictions for the batting abilities of the next opening batsman to debut for New Zealand. Additionally, we compare and identify players who excel in the role of opening the batting, which has practical implications in terms of batting order and team selection policy.