scholarly journals Flight plan for the future: floatplane pilots and researchers team up to predict invasive species dispersal in Alaska

2022 ◽  
Author(s):  
Tobias Schwoerer ◽  
Roman J. Dial ◽  
Joseph M. Little ◽  
Aaron E. Martin ◽  
John M. Morton ◽  
...  
Keyword(s):  
Invasive Species ◽  
Early Detection ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Conservation Strategies ◽  
Species Dispersal ◽  
Long Distance ◽  
Bayesian Hierarchical ◽  
Resource Managers ◽  
Response Capacity

AbstractAircraft can transport aquatic invasive species (AIS) from urban sources to remote waterbodies, yet little is known about this long-distance pathway. In North America and especially Alaska, aircraft with landing gear for water called floatplanes are used for recreation access to remote, often road-less wilderness destinations. Human-mediated dispersal of AIS is particularly concerning for the conservation of pristine wildlands, yet resource managers are often challenged by limited monitoring and response capacity given the vast areas they manage. We collected pathway data through a survey with floatplane pilots and used a Bayesian hierarchical model to inform early detection in a data-limited situation. The study was motivated by Alaska’s first known AIS, Elodea spp. (Elodea) and its floatplane-related dispersal. For 682 identified floatplane destinations, a Bayesian hierarchical model predicts the chance of flights originating from AIS source locations in freshwater and estimates the expected number of flights from these sources. Model predictions show the potential for broad spread across remote regions currently not known to have Elodea and informed monitoring and early detection efforts. Our result underlines the small window of opportunity for Arctic conservation strategies targeting an AIS free Arctic. We recommend management that focuses on long-distance connectivity, keeping urban sources free of AIS. We discuss applicability of the approach for other data-limited situations supporting data-informed AIS management responses.

scholarly journals Measuring spatial allocative efficiency in basketball

2020 ◽  
Vol 16 (4) ◽  
pp. 271-289
Author(s):  
Nathan Sandholtz ◽  
Jacob Mortensen ◽  
Luke Bornn
Keyword(s):  
Spatial Distribution ◽  
Hierarchical Model ◽  
Opportunity Cost ◽  
Allocative Efficiency ◽  
Bayesian Hierarchical Model ◽  
National Basketball Association ◽  
Spatial Context ◽  
Bayesian Hierarchical ◽  
The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

A point-based Bayesian hierarchical model to predict the outcome of tennis matches

2019 ◽  
Vol 15 (4) ◽  
pp. 313-325 ◽  
Author(s):  
Martin Ingram
Keyword(s):  
Random Walk ◽  
Hierarchical Model ◽  
Bayesian Hierarchical Model ◽  
Bayesian Hierarchical ◽  
Gaussian Random Walk ◽  
Over Time ◽  
Modelling Approach ◽  
Independent And Identically Distributed

Abstract A well-established assumption in tennis is that point outcomes on each player’s serve in a match are independent and identically distributed (iid). With this assumption, it is enough to specify the serve probabilities for both players to derive a wide variety of event distributions, such as the expected winner and number of sets, and number of games. However, models using this assumption, which we will refer to as “point-based”, have typically performed worse than other models in the literature at predicting the match winner. This paper presents a point-based Bayesian hierarchical model for predicting the outcome of tennis matches. The model predicts the probability of winning a point on serve given surface, tournament and match date. Each player is given a serve and return skill which is assumed to follow a Gaussian random walk over time. In addition, each player’s skill varies by surface, and tournaments are given tournament-specific intercepts. When evaluated on the ATP’s 2014 season, the model outperforms other point-based models, predicting match outcomes with greater accuracy (68.8% vs. 66.3%) and lower log loss (0.592 vs. 0.641). The results are competitive with approaches modelling the match outcome directly, demonstrating the forecasting potential of the point-based modelling approach.

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