Archive for January 2011

Quick Slant: Descriptive versus Predictive

January 31st, 2011 — 10:53pm

This is the first of my posts that I’ll call “Quick Slants”. These posts will be short and to the point, sometimes discussing a current event, a hot topic, or just a random topic I’m interested in.┬áIn this post, I want to discuss the difference between descriptive and predictive statistics, in preparation for my next post which will compare what wins games in the regular season versus the postseason.

A descriptive stat is one that describes the past. These types of statistics tell you what happened, not what will happen. Wins and losses are a great example; they tell you exactly what happened but they are no guarantee of the future. Other examples: Win Probability Added (a concept that originated in baseball, and implemented by Brian Burke of Advanced NFL Stats), yards gained, turnovers, and tackles.

Predictive stats are better at doing just what the name implies: predicting the future. For the most part, I am going to be more interested in predictive statistics. I’m more interested in how a player will do than how he has performed. Certainly the past can be a good indicator of the future, but it is in determining which statistics hold predictive power and which are the product of luck and randomness that the real value lies. Stats that are more predictive than descriptive are yards per play, points per possession, and Expected Points (another Brian Burke creation).

Now this is an oversimplified view, but I wanted to give a summary. In reality, statistics are some combination of predictive and descriptive. It is also, obviously, extremely important exactly what you are trying to predict. Predicting which team is going to win is certainly going to rely on different numbers than trying to determine how many yards a running back will rush for. The point is that when tackling (pardon my pun) a problem, it is important to define exactly what you are trying to do. Are you trying to describe the past? Or are you trying to predict the future? That is the first step in any process to study a question, and will guide you towards the numbers that will help you best achieve your answer.

1 comment » | descriptive, Football, predictive, Quick Slant

Rush/Pass Distribution and Drive Success, Part 1

January 28th, 2011 — 1:32am

When evaluating a team’s offense, two of the most important statistics are Yards Per Rush and Yards Per Pass. Put simply, you want to know how many yards, on average, calling a rushing or a passing play will net you. These statistics have proven to be good indicators of how many points an offense will score.

Distribution of Yards Gained on Rush/Pass Plays

While maximizing the number of yards per play is usually a good proxy, the ultimate goal of a drive is to maximize points*. (NOTE: Points here does not necessarily mean the points you score on that drive, but the net point expectancy, including field position when you don’t score.) One factor that could affect drive success over and above yards per play is the distribution of those yards. Let’s look at a graph to illustrate my point.

*Actually, it is to maximize the likelihood of a win, but I’m going to simplify that to points for now, which is very close to win maximization, especially early in games.

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5 comments » | Drive Simulator, Football, play calling, run/pass distribution

The Championship Myth

January 22nd, 2011 — 4:19pm

Full disclosure: I’m a huge Colts fan. I love Peyton Manning. Conversely, I’m not particularly fond of the Patriots or Tom Brady.

Because of this, I thought that this would be a good time to discuss what I call “The Championship Myth”, essentially the fact that many people overrate the act of winning a championship when evaluating players, especially quarterbacks. Last weekend, the Patriots–who finished the regular season 14-2 and as the top seed in the AFC thanks in large part to likely-MVP Tom Brady and the NFL’s #1 offense–were upset at home by the Jets. Following the game, there were numerous articles written about how Brady choked or was otherwise blamed for the disappointing loss. Let’s look at why this is an over-exaggeration at best and downright wrong at worst.

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10 comments » | Football, player evaluation, randomness


January 18th, 2011 — 8:13pm

If you like sports, numbers, and logic…you’ve come to the right spot. If you rely on your instincts, gut feelings, and hunches…this is probably not the place for you.

With this blog, I plan to objectively analyze sports (primarily football, but with a dash of college hoops and some baseball sprinkled in). While there will be a heavy dose of data and statistics, this is more than just a numbers site. Smart analysis needs logic, reason, and evidence to arrive at the best answer. Numbers are just an ingredient…understanding their limitations is just as important as understanding their potential. If we can harness the numbers, perhaps we can learn something about the sports we all love to watch and play. And remember, there’s only one spot on the football field to find the numbers: Outside the Hashes.

Comment » | Uncategorized

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