With a total of 31 goals, Luis Suarez won the Golden Boot award for scoring the most Premier League goals during the 2013/14 season.
I felt it was unfair that it is only success that is recognized in the annual end of season awards circuit; surely it is right that glorious failure should be recognized too?
So in that regard I wanted to create an award for the worst finisher during the Premier League season.
We know that the Golden Boot is awarded to the player that scores the most goals during the league campaign. However, we can’t simply give our award, the Sodden Boot, to the player that scores the least goals. It seems reasonable then that in looking at how a player took his chances that we make reference to the quality of those chances, ie ExpG.
The Soden Boot
The Sodden Boot is to be awarded to the player that had the worst performance in converting their attempts at goal in the Premier League this season.
I decided the cut off should be 50 shots, so I’m only including players that had more than 50 shots (including headers) at goal and I am using a ratio of Goals : ExpG to rank the players.
I’m sure that most reading this article will be familiar with the concept of ExpG, but for those that aren’t please scroll down to the bottom of this article for some brief details.
2013 / 14 Table
|Name||Team||Shots||ExpG||Goals||Goals / ExpG|
For information purposes, I have included any player that achieved an actual goals total of less than 80% of the ExpG per our model.
The inaugural winner of the Sodden Boot is Ramires of Chelsea with a dismal return of just 1 goal from his 51 shots. Based on the quality of his chances, our model expected the Brazilian to score 5 goals which results in a Goals : ExpG ratio of just 20%.
The following curve helps us realise how much of an outlier scoring just 1 goal from a sample of shots with a total ExpG of more than 5 is:
Ramires’ total of 1 goal is the red data point. Based on 10,000 simulations of the shots he took, Ramires should have scored more than 1 goal almost 98% of the time.
With his shots having an average ExpG value of 0.10 we can see that he wasn’t shooting from bad locations.
With just 12 of his 51 shots on target (24%) the Brazilian “Running Man” can’t even claim that he was unlucky in terms of opposition keepers performing heroics.
No, quite simply he just had a shocker in front of goals.
To be fair to Ramires, he has some previous form in this regard:
Many felt that West Ham’s season took a turn for the better with the return from injury of Andy Carroll. I’m on record as saying that I feel it’s more a case that Carroll’s return coincided with West Ham riding the upswing of volatility and variance. In either case he certainly didn’t add many goals to the West Ham team.
The appearance of the Spurs trio of Soldado, Townsend and Paulinho towards the top of the Sodden Boot table serves as a cursory summary of AVB’s time in charge of Spurs. The poor guy just couldn’t buy a goal.
For a man who was presumably bought to score goals, Nikica Jelavic is perched much too close to the top of that table for anyone’s liking, most of all Steve Bruce’s.
While Ramires had the worst scoring record on this measure during the 2013/14 season, he would be rightfully annoyed to be thought of as a poor finisher. His performance last season (2012/13) serves to remind us why judging goal scorers, even with the use of analytics and advance stats still has some way to go.
In 2012/13, Ramires scored 5 goals from his ExpG total of just 2.4!!!
This is what Ramires’ shot locations for 2012/13 looked like:
That’s right; in 2012/13 he scored he scored 5 goals from his 35 shots.
He went from achieving 210% of his ExpG figure last season to just 20% this season. We have seen that he was the worst finisher in the EPL this season, however last season he had the BEST Goals : ExpG ratio out of the 138 players that took more than 25 shots.
That is quite the turnaround.
Correlation of ExpG performance from Season to Season
Those numbers clearly show the enormous volatility that exists in the act of shooting. Given the relatively low probability of scoring any individual shot, players simply do not take enough shots over the course of the season to enable us (so far) to pick out the signal from the noise.
It also puts into context just how difficult it is to scout forwards. Even with the use of an advanced metric like ExpG we currently have no way of knowing if a guy that achieved just 70% of his ExpG total last season is likely to shoot at 70% or 140% next season. That just doesn’t seem right, and it’s certainly something that I’ll be trying to work towards understanding better in the future.
That being said, and just for fun, what did the Sodden Boot table for 2012/13 look like?
|Name||Team||Shots||ExpG||Goals||Goals / ExpG|
I have often seen Glen Johnson criticized for being wasteful in shooting for Liverpool; our ExpG numbers confirm that this was the case last season anyway. Johnson was the worst finisher in the EPL last season (again, based on at least 50 shots).
Once again, Jelavic appears towards the top of this table with a pretty poor return of 7 goals from an ExpG total of 12.5 last season. At this point, there is no correlation between how well or poorly a player finishes his chances from one season compared to the next, however as a striker I wonder if Jelavic can continue to post such poor numbers as he has done for the last two seasons. Just because we haven’t been able to find a correlation from season to season doesn’t mean that it’s not there
Andy Carroll also reappears on the list from last season.
This short piece had a number of purposes.
It was to serve as a data dump to show which players under performed badly in terms of dispatching their chances this season (and last), but it was also to remind our readers that we haven’t so far uncovered any meaningful correlation between the skill that a player showed in finishing their chances from one season to the next.
Just remember that, the next time your club is linked to a striker that has just come off a great season. The only thing certain in such an instance is that the player’s value will have increased as a result of his good season, but sadly, past performance is certainly no guarantee (or maybe even an indication) of future returns.
Our ExpG model looks at the specific details of each shot, including the location, whether it was a shot or a header and how it came about and assigns an objective probability of that shot being scored.