Brian Chamberlain
Advisor: Patti Frazer Lock

Modeling the Environmental Effects of Harvesting in Newfoundland Fisheries

In this paper, we provide an introduction to mathematical models of population growth and decline. We extend the models to examine the impact of different levels and types of harvesting and we discuss environmentally sustainable methods of harvesting. We look specifically at the catastrophic impact of overfishing in Newfoundland cod fisheries, once one of the richest fisheries in the world.

Laura Daley
Advisor: Mike Schuckers, Statistics

Chance vs. Skill: Assessing Shootouts in the NHL

The shootout was adopted by the NHL in the 2005-06 season. It is used in the event of a tie after five minutes of overtime, in which case three players are named for the shootout. If after the three shooters are done a tie still remains, the game goes into a ‘ sudden death’ where the game will not end until each team has taken the same amount of shots. With this change, ties are eliminated from NHL competition. This investigation looks at the shootout in the 2005-06, 2006-07, and 2007-08 NHL seasons. We analyze results for shooters and goalies to determine the probability of scoring and whether it differs significantly from player to player.

Kristen Ehringer

Advisor: Robin Lock, Statistics

The Price is Right: Using Monte Carlo Simulation to Price Stock Options

Simulations are used to imitate real-life situations when other analyses are too mathematically complex or too difficult to compute. The Monte Carlo method of simulation generates values for uncertain variables over and over again to simulate a model. The Monte Carlo simulation can be applied to many financial applications, such as the pricing of options. In the case of European options, there exists a formula, known as Black-Scholes, to price these options based on several assumptions and the results for the Monte Carlo simulation will be compared to those of the Black-Scholes equation. In addition to pricing European options, other more complex options that do not have equations to price their value will also be priced using the Monte Carlo simulation.

Andrew Ford
Faculty Sponsor: Robin Lock, Statistics

Estimating Atypical Baseball Career Trajectories

It is common in any sport for a player to continue improving throughout their career up until a certain point, at which their production will begin to fall. For example, the number of home runs a player hits generally rises until around age thirty, after which their number of home runs begins to fall. In this investigation we apply different curve fitting methods, such as loess and quadratic regression, to career statistics for individual baseball players to predict a typical career trajectory. Another question that has come up recently is whether or not the career trajectory of a certain baseball player matches that of the typical player. For example, the number of home runs a player hits could be affected by a number of different variables; ranging from being traded to a team that plays in a park with different field dimensions, to intense training in the off-season, medical enhancements, or even random chance. In our study we use statistical techniques to identify when a certain player’ s career trajectory differs significantly from that of the typical player.

Jared Fostveit
Advisor: Robin Lock, Statistics

Using Tournament Seedings to Predict the Results of the
Accenture Matchplay Golf Championship

Scott Berry wrote an article in the magazine Chance in 2000 in which he used NCAA Men’ s Basketball Tournament data from 1986-2000 to determine the likelihood of winning based on seeding, probabilities of each seed reaching each round and expected number of upsets per round. We further this approach to golf. For example, we use historical data to create a model that will predict winners of the World Golf Matchplay Championships. The golf tournament uses a 64-golfer bracket similar to NCAA basketball. We discover whether golf is more or less predictable than the “ Madness” of March.

Victor Kai- Rogers
Advisor: Robin Lock, Statistics

Measuring the Effects of 9/11: Intervention Models in Time Series

Intervention Analysis has been applied to a variety of topics in order to observe the impact of various policy implementations, regime changes and more recently effects of terrorism on time series data. There are several ways to model an intervention function namely, impulse function, gradually changing function, prolonged impulse function and a pure jump function. Specifically, I am interested in knowing if and when an Intervention Model is appropriate by observing its improvement over a univariate analysis. The main event of interest in this project is the September 11th 2001 terrorist attacks on the US and how they have affected various companies and indexes traded on the US stock exchange.

Rob LaMere
Asvisor: Robin Lock, Statistics

Determining Win Probabilities Based on Betting Lines on Sporting Events

We use betting lines and point spread systems to determine probabilities of winning in various sports. In this study we examine different types of betting for major sports such as football, basketball, baseball and hockey. We analyze data from previous seasons to assess the effectiveness of point spreads and betting lines. The goal is to use the odds-makers information to predict who will win the game and the chance of an upset.

Royce Lawrence
Advisor: Mike Schuckers, Statistics

Bowling and the Hot Hand

Hot hand, or just luck? Ever wonder why streaks come as they do? In this presentation we will discuss the results of a study of the hot hand; the tendency to perform at a higher level for a period of time. For example, a bowler may be more likely to continue to throw strikes after previous strikes. Using frame by frame bowling data and statistical methods, we will determine if the hot hand actually exists.

Dennis Lock
Advisor: Michael Schuckers, Statistics

Beyond +/-: A Rating System to Compare NHL Players

While there are many statistics, such as plus/minus, to evaluate a hockey player’s performance, few of these statistics effectively compare the worth of different kinds of players. We propose a new, more comprehensive rating method, extending the concept of plus/minus, which aims to take most aspects of a player’s game into account. Each player will be rated on the same scale, where the value of each play is determined by how that play increases or decreases the likelihood of victory. This creates a method of rating where one can compare the value of a pure goal scorer to a defensive specialist, and everyone in between.

Performance vs. Pick

Performance vs. Pick: A Study of the NBA Draft Jamie Wolff St. Lawrence University Millions of dollars are invested in the top draft picks of the National Basketball Association (NBA). A significant amount of deliberation and analysis is put into determining which athlete to draft. When making this decision, what are the most important factors to consider? This investigation will explore any patterns of recent NBA drafts considered valuable and what would be the advantage, if any, in drafting at on pick over another. We will use NBA career statistics to assess draft-day decisions based on player productivity.