Crash Course in Analytics

With the Browns filling there office with “analytics” guys like Sashi Brown and Jonah Hills’ character in Moneyball Paul Depodesta I feel many Browns fans are wondering what football analytics is and how it could help the Browns win. For one I consider myself somewhat of an expert on these stats as I have been reading books about sports stats and advanced measures ever since my tutor gave me the Bill James baseball abstracts from the mid 80s back when I was freshman in high school six years ago. From those books I learned how to answer questions about sports with stats and figures and learned the pros and cons of using certain stats to answer questions. For example, example you wouldn’t say that Carlos Santana is better baseball player than Jason Kipnis because Santana has hit more RBIs because RBI chance vary for different players in different lineup. Instead you would want to use multiple other stats or stats that are adjusted so that they aren’t affected by teammates performance.

To understand football analytics the first thing that you should understand is that all yards aren’t created equal. Odds are you already understand this as getting 5 yards on a 3rd and 4 helps the team more than getting those 5 yards when it’s 3rd and 11. The only issue is when looking at the boxscore you can only tell the total yardage and not when those yards occurred or what the value of them are. A better way of looking at plays is not just looking at the yardage but looking at how the play affected the teams chances of scoring on the drive and how the play impacted the the teams odds of winning the game.

The first stat that is critical to understanding football analytics is expected points added. Expected points added is based off the research and models of former navy pilot Brian Burke. Burke went back and looked play by play data going back years and determined how many points on average the value in points of any down and distances at any part of the field. In Burke’s own words:

“For example, if we look at all 1st and 10s from an offense’ own 20-yard line, the team on offense will score next slightly more often than its opponent. If we add up all the ‘next points’ scored for and against the offense’s team, whether on the current drive or subsequent drives, we can estimate the net point advantage an offense can expect for any football situation. For a 1st and 10 at an offense’s own 20, it’s +0.4 net points, and at the opponent’s 20, it’s +4.0 net points. These net point values are called Expected Points (EP), and every down-distance-field position situation has a corresponding EP value.”

With this model you can calculate how many expected points a player made by looking at the teams expected points before and comparing it with the expected points after. This difference is what determines the players value. The good thing about this stat is that you can guess how many points within reason a players passing, rushing and receiving contribution is. The only issue with it is that it doesn’t take into account when the points were scored.

The next stat that I feel is essential to understanding football analytics is win probability added.