Volume 16, Issue 1

Each year, the NCAA Division I Men’s Basketball Tournament attracts popular attention, including bracket challenges where fans seek to pick the winners of the tournament’s games. However, the quantity and unpredictable nature of games suggest a single bracket will likely select some winning teams incorrectly even if created with insightful and sophisticated methods. Hence, rather than focusing on creating a single bracket to perform well, a challenge participant may wish to create a pool of brackets that likely contains at least one high-scoring bracket. This paper proposes a power model to estimate tournament outcome probabilities based on past tournament data. Bracket pools are generated for the 2013–2019 tournaments using six generators, five using the power model and one using the Bradley-Terry model. The generated brackets are assessed by the ESPN scoring system and compared to those produced by a traditional pick favorite approach as well as the highest scoring brackets in the ESPN Tournament Challenge for each year.

We examine correlates of tenure length for professional soccer managers. Using 521 managers from Major League Soccer (MLS), Spain’s La Liga, and the English Premier League (EPL) whose tenures occurred between 2000 and 2015, we assess the association between both performance-related and non-performance variables, and manager duration. Performance variables include measures of a team’s ranking (or position) and relegation/promotion indicators. Non-performance variables include manager nationality and age, the timing of a manager’s hire, and the team’s wage bill. We employ survival analytic methods, including Cox’s proportional hazards model, to explore the effects of fixed and time-dependent covariates on coach tenure length. We find that La Liga managers have shorter survival, as do managers who were older when they were hired. Furthermore, finishing with a better ranking and, more importantly, improving on previous team performance yields longer survival. Most strikingly, however, we find a significant disparity in the comparison of domestic and foreign managers within a league. While the difference in longevity between domestic and foreign managers in La Liga and the EPL was minimal, American managers in MLS survived significantly longer than their foreign peers.

Predictive football modelling has become progressively popular over the last two decades. Due to this, numerous studies have proposed different types of statistical models to predict the outcome of a football match. This study provides a review of three different models published in the academic literature and then implements these on recent match data from the top football leagues in Europe. These models are then compared utilising the rank probability score to assess their predictive capability. Additionally, a modification is proposed which includes the travel distance of the away team. When tested on football leagues from both Australia and Russia, it is shown to improve predictive capability according to the rank probability score.

We propose a Markovian model to calculate the winning probability of a set in a volleyball match. Traditional models take into account that the scoring probability in a rally (SP) depends on whether the team starts the rally serving or receiving. The proposed model takes into account that the different rotations of a team have different SPs. The model also takes into consideration that the SP of a given rotation complex 1 ( K 1 ) depends on the players directly involved in that complex. Our results help to design general game strategies and, potentially, more efficient training routines. In particular, we used the model to study several game properties, such as the importance of having serve receivers with homogeneous performance, the effect of the players’ initial positions on score evolution, etc. Finally, the proposed model is used to diagnose the performance of the female Colombian U23 team (U23 CT).

Individual tennis points evolve over time and space, as each of the two opposing players are constantly reacting and positioning themselves in response to strikes of the ball. However, these reactions are diminished into simple tally statistics such as the amount of winners or unforced errors a player has. In this paper, a new way is proposed to evaluate how an individual tennis point is evolving, by measuring how many points a player can expect from each shot, given who struck the shot and where both players are located. This measurement, named “Expected Shot Value” (ESV), derives from stochastically modeling each shot of individual tennis points. The modeling will take place on multiple resolutions, differentiating between the continuous player movement and discrete events such as strikes occurring and duration of shots ending. Multi-resolution stochastic modeling allows for the incorporation of information-rich spatiotemporal player-tracking data, while allowing for computational tractability on large amounts of data. In addition to estimating ESV, this methodology will be able to identify the strengths and weaknesses of specific players, which will have the ability to guide a player’s in-match strategy.