## Attack breakdowns: League 1, 31 Dec 2018

Now that we’re (roughly) at the halfway stage of the season, I thought it was worth updating the club-by-club attack breakdowns. These are explained in detail here, but in summary they are simple scatter graphics that work as follows:

##### Explanation

Each graphic shows a club’s main attacking players: those who have:

1. Featured for at least a quarter of their total pitch minutes in the league this season, and
2. Taken an average of at least one shot per game.

The size of each player’s bubble is proportional to the percentage of possible minutes that they’ve played.

Each player’s bubble is plotted on a chart with the two axes working like this:

• On the horizontal axis we have their goal threat, based on the “expected goals” value of shots taken per 90 minutes. This is effectively a measure of the combined quality of their goalscoring chances.
• On the vertical axis we have their scoring rate, using a less abstract measure of actual number of goals scored per 90 minutes.

Both axes exclude penalties, as those can massively skew a player’s contribution away from the threat they pose from open play.

There’s a shaded “stripe” which indicates the long-term shot conversion rate of all finishers except the top and bottom 10%, so we can identify those whose performance may be unsustainable (i.e. unlikely to be repeated next season). If a player is above the stripe, they’re converting chances at a rate consistent with someone in the top 10% of finishers, and likewise a player below the line is in the worst 10%. Based on what we know about the specific player, we can therefore take a view on whether we expect their scoring rate to continue.

##### Club-by-club graphics

Cauley Woodrow didn’t make it onto the pitch for Barnsley until November but he’s set the standard ever since with the chances he’s been getting. In terms of raw finishing it’s been Sunderland‘s Josh Maja making the headlines – I hope he stays until at least the end of the season as he’s been scoring at around twice the rate I’d expect from the data and I want to see if he can keep it up. Tom Eaves of Gillingham has also been converting chances at an impressive rate.