Search

One of the most important and challenging problems in football is predicting future player performance when transferred to another club within and between different leagues. In addition to being the most valuable prediction a team makes, it is also the most complex analytics task to perform as it needs to take into consideration: a) differences in playing style between the player's current team and target team, b) differences in style and ability of other players on each team, c) differences in league quality and style, and d) the role the player is desired to play. In this paper, we present a method which addresses these issues and enables us to make accurate predictions of future performance. Our Transfer Portal model utilizes a personalized neural network accounting for both stylistic and ability level input representations for players, teams, and leagues to simulate future player performance at any chosen club. Furthermore, we use a Bayesian updating framework to dynamically modify player and team representations over time which enables us to generate predictions for rising stars with small amounts of data., Comment: 25 pages, 15 figures

The ability to predict what shot a batsman will attempt given the type of ball and match situation is both one of the most challenging and strategically important tasks in cricket. The goal of the batsman is to score as many runs without being dismissed, whilst for bowlers their goal is to stem the flow of runs and ideally to dismiss their opponent. Getting the best batsman vs bowler match-up is of paramount importance. For example, for the fielding team, the choice of bowler against the opposition star batsman could be the key difference between winning or losing. Therefore, the ability to have a predefined playbook (as in the NFL) which would allow a team to predict how best to set their fielders given the context of the game, the batsman they are bowling to and bowlers at their disposal would give them a significant strategic advantage. To this end, we present a personalized deep neural network approach which can predict the probabilities of where a specific batsman will hit a specific bowler and bowl type, in a specific game-scenario. We demonstrate how our personalized predictions provide vital information to inform the decision-making of coaches and captains, both in terms of pre-match and in-game tactical choices, using the 2019 World Cup final between England and New Zealand as a case study example., Comment: 20 pages, 12 figures, accepted paper for the 2020 MIT Sloan Sports Analytics Conference

In the last 25 years there has been an important increase in the amount of data collected from animal-mounted sensors (bio-probes), which are often used to study the animals' behaviour or environment. We focus here on an example of the latter, where the interest is in sea surface temperature (SST), and measurements are taken from sensors mounted on Elephant Seals in the Southern Indian ocean. We show that standard geostatistical models may not be reliable for this type of data, due to the possibility that the regions visited by the animals may depend on the SST. This phenomenon is known in the literature as preferential sampling, and, if ignored, it may affect the resulting spatial predictions and parameter estimates. Research on this topic has been mostly restricted to stationary sampling locations such as monitoring sites. The main contribution of this manuscript is to extend this methodology to observations obtained by devices that move through the region of interest, as is the case with the tagged seals. More specifically, we propose a flexible framework for inference on preferentially sampled fields, where the process that generates the sampling locations is stochastic and moving over time through a 2-dimensional space. Our simulation studies confirm that predictions obtained from the preferential sampling model are more reliable when this phenomenon is present, and they compare very well to the standard ones when there is no preferential sampling. Finally, we note that the conclusions of our analysis of the SST data can change considerably when we incorporate preferential sampling in the model., Comment: 38 pages, 12 figures, accepted for publication in the Annals of Applied Statistics