Explaining “Return Vs Expected”

“One running back runs for three yards. Another running back runs for three yards. Which is the better run? This sounds like a stupid question, but it isn’t. In fact, this question is at the heart of nearly all of the analysis on Football Outsiders.”

 This quote comes from Football Outsiders’ first line describing their propriety DVOA stat that compares one NFL play to another. They go on further to state that

 “Several factors can differentiate one three-yard run from another. What is the down and distance? Is it third-and-2 or second-and-15? Where on the field is the ball …. Conventional NFL statistics value plays based solely on their net yardage.”

 (If you want to read more go to http://www.footballoutsiders.com/info/methods. If you are just interested in the NFL I would strongly recommend that you visit the site and use it as a counterweight to the traditional analysis on the game.)

 The site got me thinking on how we view GAA and especially the scoring in a game. Bernard Brogan gets 0-06 in a game against Louth with 3 from frees. Darren Clarke gets 0-08 from frees in the same game. Has Clarke had a better game just because he scored more frees? Were all the frees in front of the posts? What if Brogan’s 3 points from play were from out on the sideline? How do we compare the two performances? As such I started to chart games with a view to comparing team’s attacking play.


Every shot charted is broken down into two main component parts

         Type of shot: whether the shot was from play or a deadball

         Segment: where on the pitch the shot was taken from

 This led to 18 categories of shot (9 segments x 2 types ). Once all the shots had been compiled I was then able to give an average success rate for each of the 18 shot categories. This is, inelegantly, explained in the very first few posts on the blog.

 The next stage was to attempt to give a value to each shot. We know a shot from out on the wing is harder to convert than a shot from in front of the posts on the 21m line. But by how much? And conversely when a player misses a shot how do we compare misses across the segments and against success in the same segment? We must chart failure as well as success.

 Again I turned to Football Outsiders. The basis for some of their returns is comparing a play to the average and working from there. After compiling enough data (3,392 shots in 61 games over the 2010 & 2012 Championships) I was in a position to compile an average success rate for the 18 shot categories. This meant that I could create a return for each shot by comparing the outcome to what was expected (expected in this case means the average for each of the 18 shot categories). In the absence of any meaningful name I christened this return the “Return Vs Expected” (or “Vs Expected”). How this return is compiled is explained below

 Return Vs Expected

The average return for a shot type within a sector = x%. This is then represented as a decimal. Eg if the average success rate = 65% this then becomes 0.65

Weighting for a score = (1-x) or (1-0.65) or +0.35

Weighting for a miss = (-x) or -0.65


Using a concrete example ; the average return for frees from Sector 5 is 84.5%(131 scored from 155 attempts). This is converted to a decimal, so that 84.5% = 0.845 therefore

A shot that scores = (1 – x) or (1 – 0.845) = +0.115

Whilst a shot that misses =  (-x) or -0.845


The average player will score, out of 100 attempts, from the same free 84.5 times. So therefore the more times a player hits that shot over 84.5 the more positive a return he will have and the more times he misses the more negative a return he will have

Same shot is hit 95 times = (95 * 0.155) + (5 * -0.845) = +10.5

Same shot hit 90 times = (90 * 0.155) + (10 * -0.845) = +5.5

Same shot hit 85 times = (85 * 0.155) + (15 * -0.845) = +0.5

Same shot hit 80 times = (80 * 0.155) + (20 * -0.845) = –4.5

Same shot hit 75 times = (75 * 0.155) + (25 * -0.845) = –9.5


We now have a method for giving a weighting to every shot – thus we can compare (and rank!) very disparate performances.


One Response to “Explaining “Return Vs Expected””

  1. Review of Basics | dontfoul Says:

    […] Weighting (also called “Return Vs Expected” or ”Vs Expected”) Ultimately whilst useful none of the above stats go any way towards informing us about the type of shots taken. If the average return for shots from play is 46.5% how good a day did the player scoring 3 from out on the wings have? Similarly is getting 4 from 8 (50%) from in front of goals an “average” day? The weighting attempts to rank these two very different shooting displays against the average (how the weighting is created can be viewed here). […]

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