- Home Archives for April 2014
I've created a tool for predicting when players will come off the board. This isn't a simple average of projections. Instead, it's a complete model based on the concept of Bayesian inference. Bayesian models have an uncanny knack for accurate projections if done properly. I won't go into the details of how Bayesian inference works in this post and save that for another article. This post is intended to illustrate the potential of this decision support tool.
Bayesian models begin with a 'prior' probability distribution, used as a reasonable first guess. Then that guess is refined as we add new information. It works the same way your brain does (hopefully). As more information is added, your prior belief is either confirmed or revised to some degree. The degree to which it is refined is a function of how reliable the new information is. This draft projection model works the same way.
A guest post by W. Casan Scott, Baylor University.
As different as ecology and the NFL sound, they share quite similar problems. The environment is an infinitely complex system with many known and unknown variables. The NFL is a perpetually changing landscape with a revolving door of players and schemes. Predicting an athlete’s performance pre-draft is complicated through a number of contributing variables including combine results, college production, intangibles, or how well that player fits a certain NFL scheme. Perhaps techniques that ecologists use to discern confounding trends in nature may be suitable for such challenges as the NFL draft. This article aims to introduce an eco-statistical tool, Principal Component Analysis (PCA), and its potential utility to advanced NFL analytics.
My Ph.D. research area is aquatic eco-toxicology, where I primarily model chemical exposure hazards to fish. So essentially, I use the best available data and methods to quantify how much danger a fish may be in, in a given habitat. Chemical exposures occur in infinitely complex mixtures across many different environments, and distinguishing trends from such dynamic situations is difficult.
Prospective draftees are actually similar (in theory) in that they are always a unique combination of their college team, inherent athleticism, history, intangibles, and even the current landscape in the NFL. The myriad of variables present in the environment and the NFL, both static and changing, make it difficult to separate the noise from actual, observable trends.
In environmental science, we sometimes use non-traditional methods to help us visualize what previously could not be observed. Likewise, Advanced NFL Analytics tries to answer questions that traditional methods cannot. The goal of this article is to educate others of the utility of eco-statistical tools, namely Principal Component Analysis (PCA), in assessing NFL draft prospects.
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For those not familiar with lacrosse, imagine hockey played on a football field but, you know, with cleats instead of skates. And instead of a flat puck and flat sticks, there's a round ball and the sticks have small netted pocket to carry said ball. And instead of 3 periods, which must be some sort of weird French-Canadian socialist metric system thing, there's an even 4 quarters of play in lacrosse, just like God intended. But pretty much everything else is the same as hockey--face offs, goaltending, penalties & power plays. Lacrosse players tend to have more teeth though.
Because players carry the ball in their sticks rather than push it around on ice, possession tends to be more permanent than hockey. Lacrosse belongs to a class of sports I think of as "flow" sports. Soccer, hockey, lacrosse, field hockey, and to some degree basketball qualify. They are characterized by unbroken and continuous play, a ball loosely possessed by one team, and netted goals at either end of the field (or court). There are many variants of the basic team/ball/goal sport--for those of us old enough to remember the Goodwill Games of the 1980s, we have the dystopic sport of motoball burned into our brains. And for those of us (un)fortunate enough to attend the US Naval Academy (or the NY State penitentiary system) there's field ball. The interesting thing about these sports is that they can all be modeled the same way.
So with lacrosse season underway, I thought I'd take a detour from football work and make my contribution to lacrosse analytics. I built a parametric win probability model for lacrosse based on score, time, and possession. Here's how often a team can expect to win based on neutral possession--when there's a loose ball or immediately upon a faceoff following a previous score:
The main responsibilities of the intern will include, but are not limited to the following:
*Support ANS team with data extractions, analysis, reporting, and data presentation
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*Currently enrolled in, or a recent graduate of, a quantitative degree program such as Operations Research, Mathematics, Statistics, Economics, Computer Science, Information Science, Management Information Systems, or similar
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