Expected Goal Chains: The Link between Passing Sequences and Shots / by Kevin Shank

By Kevin Shank (@Kev_Shank)

For those who are not familiar with Expected Goal Chains (xGC), the metric looks at all passing sequences that lead to a shot and credits each player involved with the xG. Instead of just looking at expected goals and expected assists, which primarily benefits strikers and attacking midfielders, xG Chains is beneficial to every player involved in a sequence. Most importantly xGC credits those defensive or two-way players who are integral to a play’s build-up but don’t necessarily serve that final key pass. To calculate xGC, I assembled every pass, shot, foul, and defensive action so far in MLS and assigned a unique ID to each passing sequence. When a sequence ended in a shot, each player is attributed with the xG from that shot. StatsBomb defines it very succinctly, so the below steps are stolen directly from them: 

  1. Find all the possessions each player is involved in.
  2. Find all the shots within those possessions.
  3. Sum their expected goals.
  4. Assign that sum to each player, however involved they were.

To the right is a play from Chicago on June 17 when they won 2-1 at New England. This 20-pass sequence between eight players ends with a Luis Solignac shot with .324 xG; therefore, each player in the chain gets xGC value of .324. Even though Joao Meira had only one pass in the sequence compared to Matt Polster’s five passes, both players get the same xGC for their involvement.

So who are the top players that are contributing to their team’s attack? Not surprisingly the top five players as of 9/25 include the elite of MLS attacking prowess:

Row LabelsxG ChainMinutesxGC/96xGxAxG+xA
Nicolas Lodeiro 26.95 2923 0.89 5.45 9.92 15.38
Nemanja Nikolic 24.79 2881 0.83 19.35 1.47 20.81
Joao Plata 24.18 2245 1.03 12.23 6.78 19.02
David Villa 23.3 2493 0.9 13.78 5.96 19.74
Diego Valeri 22.9 2810 0.78 12.29 6.17 18.46

Some of these players already have a great xG+xA contribution so it’s worth questioning if xGC is adding any more in evaluating those players. Well, another benefit of this metric is that by subtracting xG and xA* from a player’s total xGC, we can view their importance to their team’s buildup play. Essentially, by removing that final shot or final pass, the xGC Buildup values passes anywhere from the start of the sequence to the secondary key pass.

Row LabelsxG ChainMinutesxGC/96xG+xAxBuildup GCxBuildup GC/96xBuildup %
Nicolas Lodeiro 26.95 2923 0.89 15.38 11.57 0.38 43%
Michael Bradley 12.74 2651 0.46 2.68 10.07 0.36 79%
Bastian Schweinsteiger 12.91 2091 0.59 3.67 9.23 0.42 72%
Dax McCarty 12.33 2415 0.49 3.2 9.13 0.36 74%
Sacha Kljestan 19.79 2553 0.74 10.93 8.85 0.33 45%

In addition to further showing that Lodeiro is amazing, xGC shows the importance of players like Bradley and Schweinsteiger, who do not have impressive xG+xA, but are integral in creating those passing sequences that end in shots. Overall this metric adds more context to evaluating players by looking at the bigger picture of passing sequences and all players’ xGC. It can can be viewed on ASA by selecting it from "Player Passing" drop-down in the menu options and will (hopefully) be updated regularly.

*An important adjustment that was made in calculating xA compared to elsewhere on ASA is that I am not counting redirected shots as key passes. On instances when there is a redirected shot, a player would receive xG for the initial shot and xA once the shot is redirected which seems like double-dipping to me, so only passes that lead to a shot get credited with xA.