In this post I will be taking a look at the concepts of xG Chain (xGC) and xG Buildup (xGB), why they are useful and how we can develop these concepts to get even more use from them. Both of these concepts further the expected goals (xG) and expected assists (xA) metrics, allowing the contribution of players not directly involved in a goal to be accounted for.
xG is a likelihood attached to each shot that attributed the chance of that shot being a goal. This metric is only really useful for players who take lots of shots, such as forwards.
xA is attached to a pass that immediately precedes a shot, the xA measures the likelihood that a pass will become an assist from the following shot.. This metric aims to widen the influence of the xG metric and attribution of play to the creative players who create the shots that the xG provides information for.
Both of these are intuitive and simple concepts that provide an estimate for specific actions on the pitch. Since goals and assists are key events in a match, it makes sense to focus analysis on them since they are incredibly predictive. xG and xA are very limited however, they only care about a shot and the preceding pass so don’t tell us anything about any of the play that happens leading up to there. It turns out that the majority of football isn’t just taking turns taking shots, so it would be nice to be able to do something like xG/xA for other actions on the pitch.
Just as xA is to xG; attributing the result to the preceding pass, xG Chain is to xA where it aims to do the same thing for the whole preceding possession chain. In this way you can widen the influence of xG to all players that are involved in the preceding possession. Where xG mainly highlights forwards and xA mainly highlights creative players, xG Chain aims to highlight players that make contributions to the possessions that end up with a shot. These could include your ‘assisting the assister’ players, your deep lying playmakers like Jorginho who get criticised for lack of assists or your progressive passing defenders that wouldn’t usually get the credit they potentially deserve for starting effective possessions.
Calculating xG Chain: https://statsbomb.com/2018/08/introducing-xgchain-and-xgbuildup/
- Find all possessions each player is involved in
- Find all shots within those possessions
- Sum the xG of those shots (usually take the highest xG per possession)
- Assign that sum to each player, however involved they are
You can normalise xGC per 90mins to see contributions per match, however this still highlights forwards and creative players since if they are the players getting the shots then they will get all the credit for their own shots plus any other possession chains they are involved in.
Since the aim is to highlight players that xG and xA don’t directly pick up, you can calculate xGC without including the shots and assists to get xG Buildup. This leaves all of the preceding actions to the assist and the shot, or all of the build up play as it were. By removing assists and shots, the dominance of forwards is removed and the remaining players are heavily involved in all the play up to just before the defining assist and shot. You can also normalize xGB per 90 mins to see contributions per match. Again, each player involved gets equal contribution as long as they are involved in the possession chain in some way.
xG Chain and especially xG Buildup are great metrics that highlight the contributions of players leading up to assists and shots. They allow players that don’t contribute directly to goals to make a case for their own importance. Normalising per 90 mins is an effective way to allow for reduced player minutes due to injury or substitutions, and evaluate all players on the same basis.
As great as the concepts of xGC and xGB are, there is a clear and influential flaw in the calculation when assigning the xG of the possession chain to the players involved. Each player gets equal contribution no matter how involved they were. So player A makes a simple 5 yard pass in their own half gets the same assigned contribution as player B who made the decisive through ball to a player who squared it for an open goal. Neither player would get credit in xG/xA but both would get the same xGC/xGB contribution despite the fact that player A’s contribution was potentially arbitrary and player B’s turned the possession chain from probing to penetrating and a shot on goal.
Another way to consider the contributions of each player is if you were to remove the action of that player, how likely was the possession chain to have still occurred. If you remove player A’s simple pass, it doesn’t take much for the possession chain to maintain its low threat whereas if you remove player B’s decisive through ball then it’s unlikely that the possession chain continues in the same way. In this way, player B’s contribution could be argued to be more important than player A’s.
This leads to considering other ways of normalising xGC and xGB, each method of assigning contribution and normalising will highlight different aspects of the build up.
Since you have all the information of each possession chain, you may have access to the number of passes or touches that each player contributed to the chain. If you proportion the xGC out by the frequency of passes or touches you can get a good idea of the proportion of involvement that each player has in each possession chain. For example, if a possession chain involves two players, C and D, where player C made 3 passes and player D made 4 passes with a resulting shot that has an xG of 0.7. Then player C contributed 3/7 passes so gets an xGC of 3/7 * 0.7 = 0.3 and player D contributed 4/7 passes so gets an xGC of 4/7 * 0.7 = 0.4. Since player D was involved slightly more than player C then player D gets a higher xGC. A similar calculation can be made using touches which will consider players who dribble more than just counting passes.
You aren’t limited to just counting passes or touches of the ball, you can get more creative with the allocations if you want to credit specific types of actions. You could only count progressive passes that move the ball forward by at least 10 yards, try to quantify the most important or necessary actions of a possession chain (decisive through ball/taking on a player in the box) or count the number of opposition players taken out of the game by each player involved, where ‘taking a player out the game’ may be defined as moving the ball closer to the defending team’s goal than the player.
xG Chain and xG Buildup are both intuitive and simple metrics that assign contributions to players that don’t get directly involved in taking shots or assists but are frequently involved in preceding actions to these events. On their own they can already highlight players that seem to contribute well under the ‘eye-test’ when you watch them, but they can be misleading and provide many false positives since all actions are considered equal under xG Chain.
Credit to Statsbomb and Thom Lawrence for introducing concepts and providing clear explanations and examples. They even include free data sets for FAWSL and the 2108 FIFA World Cup if anyone wants to try themselves. Check them out here: