In previous posts I have looked at the effect of pressure on passing {link to previous post}. The analysis in that post included distance of the pass, but it did not qualify for the location of the pass. Additionally, that analysis was focused on the probability that the pass would be completed, instead of the value that the pass created for the team. This time, I’ve taken a sample of over 130,000 passes from the Brazilian Serie A and examined the effect that each pass has the odds that the team will score a goal, based on where on the field the pass originated and where it was received. In order to make the analysis at all tractable, I split the pitch into 28 distinct zones. The zones are detailed in the diagram to the right. The orientation of the diagram is such that the lowered numbered zones are the defensive zones (a team’s defensive penalty box consists of zones 2, 3, 4 & 5). Zones 19 and higher make up the attacking zones.
With the StatDNA play-by-play data, I was able to look at the probability that a team will score on a given possession, given the location, and a host of other variables. This estimated probability (or expected value/EV discussed in previous posts) changes with each play, so the change in the probability can be calculated with each pass. Incomplete passes and passes that reduce the probability of scoring create negative changes, while passes that increase the odds of scoring are positive changes. Separating the pass into their proper categories and averaging the change in scoring probability, we can see the average value of a pass between any two zones.
The table below provides the estimates the average change in expected value from a completed pass by originating zone to receiving zone for the Serie A data. White zones represent a change in EV less than 0.3%, Yellow zones represent a change in EV from 0.3% to 2%, light green zones from 2-10% and dark green zones greater than 10%. Note also that some boxes are white simply because very few passes occured between those two zones. For example, a pass from zone 16 that finds its way to zone 25 (inside the attacking box) increases the probability that the team will score a goal by 11.2%, while a pass from zone 16 to zone 20 increases the probability that the team will score by 0.78%.
PASS VALUE CREATION FROM RECEIVING ORIGINATING LOCATION TO RECEIVING LOCATION
A couple of interesting things emerge. Passes from zone 23 or 28 – which are generally crosses – have about half the increase in EV that passes from zones 20 and 21 into the box have (4% vs 8%). When we include incomplete passes (and crosses) in the EV analysis – we see that passes in from the wing have an average EV improvement that is much lower than the average EV of passes from zones 20 and 21 into the box. Since incomplete passes have negative value that counteracts the positive value from our completed passes, one would expect that passes from different bordering zones into the box would have similar average values when both incomplete and completed passes are included. This leads us to believe that Brazilian Serie A teams could be crossing the ball more than they should be and emphasizing play up the middle of the field less than they should be.
Another thing that is interesting is that passing from the defensive 1/3 into the midfield increases EV very little (in fact we didn't even include these passes in the table above, which begins in the midfield); passing from the midfield to the attacking 1/3 increases value a moderate amount and then of course passing from the attacking 1/3 and wings into the box increase EV substantially. We aggregate a player’s EV contribution to the team over all of his actions (including his complete and incomplete passes) and at first glance this may seem unfair to defenders who are seemingly receiving no value for their passing from this chart.
This, however, is not really the case. A defender is receiving value for two facets of passing: firstly, since he can receive a large negative value for an incomplete pass in the defensive 1/3, high completion %’s will tend to aggregate small increments of value over time and avoid large negative EV contributions. Second, and perhaps more importantly, this chart only takes into account increases in EV due to movement of the ball between field zones, when in fact the EV is multi-dimensional. We will give the defender higher positive EV when he passes the ball to a more favorable location in terms of level of defense pressure on the recipient for example – so a defender who consistently passes to players who are more open will aggregate higher EV over time.
On the opposite end of the spectrum, an attacking midfielder will have a much higher degree of variance in his EVs – with one very high EV completed pass into the box, counteracting lots of negative EV incomplete passes. In this case we value someone who can over time aggregate a high EV in spurts. Of course, if too many high value passes are attempted without success, this player’s value contribution will suffer.
This break down of passes also allows for clearer look at how teams can use their passing games to create more offense. Charts like the one below can be generated for different types of pressure as well as how deep into the defense the attacking player is at the time of the pass, to further flesh out the effect of passing. Additionally, these values can be summed up at the team and player level to examine which teams have the most effective passing games and where they create most of their value. At the player level, again the total value and where the value is created can be examined.