Author: Rennie Heza (page 2 of 2)

Week 2 – Rennie Heza

Ignorance, in the realm of digital scholarship, is not taking on a project too large, or asking a question too difficult to answer. Instead, ignorance is allow yourself to believe a project is perfect.

As I talked through project ideas with library staff this week, the main goal of writing my project charter remained the same: seek to answer a question you would like to see solved, and worry about the details later. After a week of struggle, I am proud to share my first official proposed project summary:

 

“Hockey is undeniably old, but that doesn’t mean the game isn’t constantly advancing. In recent history, play has become quicker, and strategy has changed immensely, but the goal of each team year in and year out has remained the same: win the Stanley Cup. The Stanley Cup is the championship trophy of North America’s National Hockey League (NHL), widely regarded as the best hockey league in the world. Each of the 30 current NHL teams will do anything to gain a competitive edge over their opponents. The newest arms race in the world of sports: advanced data analysis. But answering the question: “What does a team have to do to win?” is far from simple.

This project is intended to compare the performance of each NHL team during the 2016-2017 regular season, and further, during the 2017 Stanley Cup Playoffs. Through regression and correlation analysis, the project will first identify key factors and metrics involved in constructing a successful NHL roster, in both the regular season and the playoffs. Though similar, I expect the main factors contributing to success in these two realms will differ, leading to interesting decisions for team management when analyzing a team’s potential.

Then using this data, a model will be created to predict a team’s success in both the regular season and playoffs. This ultimate goal is an interactive model that allows fans, players, and general managers the ability to track their teams projected success given roster moves, player injuries, line shuffling, etc. Using simple inputs (the metrics determined to be most influential in the first portion of the project), the model will graph a team’s projected future performance in terms of regular season wins and playoff wins, player scoring, and goalie performance. This interface will ensure that even the most casual hockey fans can understand the purpose of the model, without having to input or output complex metrics. However, the implications of such a model could involve high-level personnel decisions. Thus, this project will appeal to a wide range of individuals.

This outwardly simple model will cater to fans and hockey professionals alike, while providing statistically backed predictions of team performance. The goal is to identify the building blocks needed to create a contender of a team, and then allow fans and hockey professionals to fiddle with a team’s makeup to identify the best strategy for any NHL team to improve their season predictions. This will allow complex analysis to be performed on any team, by anyone, with Internet access. Such as tool has yet to be created, but this summer, that will change.”

 

Far from perfect? Sure. A project I can’t wait to begin? Absolutely.

Week 1 Blog

Week 1 of the DSSRF program has been whirlwind of meeting Bucknell’s library staff, hashing out project ideas, and becoming familiar with the tools we will be using this summer. Though busy, the week has provided me strong reassurance that the DSSRF program is the great opportunity my academic advisor brought to my attention months ago. This February, while unsuccessfully trying to navigate the endless job boards, Mathematics Professor Nathan Ryan, my dedicated academic advisor, alerted me of the DSSRF program, highlighting the structured yet individualized research that could be done through this program.

To understand why this program is such a great fit for me, I must explain my intended area of reserach. I have long been interested in sports analytics. As a child, I memorized batting averages in baseball, save percentages for hockey goalies, and other seemingly useless numbers. It was only recently I realized this lifelong passion could blossom into a career. Given no other programs on Bucknell’s campus cater to this interest of mine, the DSSRF program is the perfect opportunity to get a taste of research while keeping my career goals in mind.

Through the application and interview process, I was hesitant to fully commit to a project idea. A successful eight weeks of research hinges on an intriguing, yet achievable research question. This pressure to land on the perfect research topic gnawed away at me in the weeks leading up to the program’s start. However, upon meeting with our program facilitators, Courtney Paddick and Carrie Pirrman, my nerves calmed. I was reminded that a research question is not set in stone. Projects adapt, researchers constantly strive to overcome whatever barriers they may face, and adjust as need be.

Throughout this week I have narrowed the project idea I initially brought to the application process. With the help of the program facilitators and the input of my fellow researchers, I now know that I want to create a visual data comparison of NHL teams, linking individual performance metrics with team success. In my opinion, the challenge is to present overwhelming amounts of data, to anyone, a hockey fan or otherwise, in an understandable format. This allows every viewer to understand the metrics driving the modern game of hockey. Though I am sure my project will change as the summer goes on, I am proud to have landed on an idea which I am confident will yield an interesting result. I owe great thanks to my advisor for bringing this wonderful opportunity to my attention, to my research peers for proposing project tweaks all week, and to the program facilitators for believing that this project had potential from the beginning. Though we’re only a week in,  I am confident this summer will prepare us well for whatever is to come.

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