Monday 30 December 2013

RACE to Goals Model – The Defence

Prior to the season starting, I introduced the RACE to Goals Model, which you can find here, and I suggest you have a read of that before you continue with this one if you want to have a full description of the different metrics and how they are calculated.
Essentially, I am looking at the same metrics, but this time flipped to a defensive point of view, so the rate of shots conceded, the Defensive Efficiency, and the conversion of chances conceded by each type.
I will describe Defensive Efficiency here though, as it’s calculated slightly differently. Whereas Creative Efficiency attempts to show how good a team is at creating good chances, measured as the proportion of Clear Cut Chances to Total Shots, Defensive Efficiency attempts to show how good a team is limiting the amount of good chances the opposition has, and is measured as the proportion of Normal Chances conceded to Total Shots conceded (%NC). So the higher the number, the lower the percentage of Clear Cut Chances conceded, and the more efficient the defence is.
The benchmark numbers are essentially the same, the slight difference being own goals, and those ‘shots’ by players on the defending team that lead to own goals are also included.
The table below shows how well the teams performed last season against the 4 metrics.



The team that conceded the fewest shots was Tottenham, with only 370 over the entire season, so a touch under 10 shots a game. At the other end of the scale were Reading, who conceded 706 shots, the worst by over 60 shots.
Like with the original article, I feel the raw numbers in the table are a little hard to read, so again I’ll add context and measure each metric as the percentage difference from the benchmark team. From the a defensive point of view, having shots and conversion rates below the benchmark is good, but this is not the case for Defensive Efficiency, so I’ve highlighted this in the table as anything in red as being ‘bad’.

As with Creative Efficiency, Manchester United also had the best Defensive Efficiency, limiting their opponents to only 8.2% of their shots coming from CCCs, with Manchester City being the only other team to have a Defensive Efficiency of over 90%, seeing them perform 6% and 4% better than average respectively. The team with the worst Defensive Efficiency was Newcastle, who allowed over 18.5% of all chances against to be CCCs; however the 2nd worst team, perhaps surprisingly considering how few shots they conceded, was Tottenham, allowing almost 18%, and possibly showing the risk of playing with a high defensive line.
Looking at the conversion rates it becomes clear why Wigan struggled last season. They had by the worst rate of CCCs conceded, in fact at 52.7%, they are the only team over the 3 years of data that conceded more than half the CCCs that they faced. They were also the 2nd worst at stopping Normal Chances being conceded. Reading actually had the best rate when it came to stopping CCCs in the league, but unfortunately for them, when you allow the opposition to create over 100 CCCs in total, you will still concede a lot of goals.
No teams outperformed or underperformed all 4 of the metrics compared to the benchmark. Only 4 teams, the two Manchester clubs, Chelsea and Swansea outperformed on 3 of the benchmarks. Liverpool join Utd, City and Chelsea as the only teams who conceded fewer shots than the benchmark whilst also having a higher than average Defensive Efficiency. Despite conceding the fewest shots, we can see why 7 teams conceded less goals than Tottenham following their underperformance in the 3 other metrics.

Converting the metrics into Expected Goals, we see how badly Wigan performed. Whilst they would have been expected to concede just less than 54 goals from the shots that the opposition had, which was only the 10th lowest, they actually conceded 73 (+19.1 goals more than expected). The other big underperformers were Southampton (+9.7 goals), Newcastle (+9.0 goals) and Aston Villa (+7.9 goals). The biggest overperformers were Everton (-9.5 goals), Sunderland (-8.7 goals), Stoke (-6.4 goals) and Arsenal (-6.0 goals).
In my next posts I will combine some of the attacking and defending metrics together to analyse team’s performances in some new ways, and see how the teams have performed so far this season.
This was originally posted on  EPLIndex  http://eplindex.com/43205/race-goals-model-defence.html

Defending Liverpool's Defence

With the season about to start, I thought I would follow up to piece that I did earlier in the year looking at how Liverpool’s form changed over the season, however whilst that looked at attacking form, this one looks at Liverpool’s defensive form. Again I will look at Liverpool’s performance compared to how the league performed on average, how the top 4 performed, and also compared to Liverpool in the 2011-12 season, as well as having the short term form by having the 6-game moving average. One thing to note is that due to there being fewer observations, for example Liverpool conceded far fewer shots, goals etc., that the graphs show more extreme changes compared to the attacking versions of these graphs
I’ll start by looking at shots conceded per game. Apart from the 18 shots conceded in the first game of the season against West Brom skewing the averages, Liverpool performed more or less in line with the Top 4 teams throughout the season.

In terms of the accuracy of opposing team’s shots, despite the slow start that Liverpool had and perhaps surprisingly, they actually allowed significantly less shots to hit the target compared to the Top 4 teams and the rest of the league over the first half of the season, whilst over the 2nd half of the season, a greater percentage of opponents shots were hitting the target.

Moving on to Opponent Shots Conversion and Shots on Target Conversion, we can see how poorly Liverpool defended and Pepe Reina performed in the opening 5 or 6 games of last season. Basically, Liverpool defended and kept goal more or less like a lower league team when going up against a Premier League side in a cup, but this quickly regressed to the mean, and they performed like a Top 4 team from game 7 onwards (in the moving-average plot, this shows up from match 12). Those first 6 games had such an impact though that the end of season conversion rates were still only in line with the league as a whole.


How do Liverpool, or more pertinently Pepe Reina and Brad Jones, do at keeping out Clear Cut Chances (CCC)? So what is a CCC? It is one of Opta’s few subjective stats that can broadly be described as a chance where the attacker is probably central to goal with only the keeper to beat. So a keeper would hope to either save it, or perhaps attempt to put the attacker off sufficiently that they miss. As I mentioned in the original piece, the conversion rate for CCCs is much more variable than the other conversion rates, this is because in some games there will be few or even no CCCs, which means that both very high and very low single game conversion rates are far more likely, and we see this clearly in Liverpool’s form plot (note that the reason you can’t see the league average plot is because it was the same as the Top 4). Again, Liverpool started poorly, but were better than the Top 4 teams from match 7 onwards, apart from a large peak at match 17 where all the CCCs that Liverpool faced were scored giving a 100% conversion rate. More specifically, it was in fact a 4 match period with the goals coming from Tottenham, West Ham and Aston Villa.

With that in mind, it is interesting to then see the rate at which Liverpool were giving up CCCs per game through the season. Again we see Liverpool started off poorly, giving away on average 1.5 CCCs over the first 10 games, but by match 17, where we saw the 100% conversion rate, the 6-game form had fallen to 0.7 per game. So, it was only 4 out of 4 CCCs conceded in 6 games. As the average conversion rate for all CCCs is around 38%, it is a bit like tossing a coin 4 times and getting 4 heads, so I don’t think we should put it down to poor goal keeping. You’ll notice there is a sharp rise in CCCs conceded from about match 20, but this coincided with an increase with CCCs for Liverpool, and can perhaps be put down to increased attacking leaving the defence more open (Note: Liverpool’s average in 2011-12 was the same as the Top 4’s last season).

Finally I’m going to look at Errors per Game. It should be noted that these are ‘on the ball’ errors, so does not include an error like not marking the run of an opponent from crosses (something that had many Liverpool fans pulling their hair out). Again we see the effect that Liverpool’s poor start to the season had, however it took longer for Liverpool to recover from than compared to the other metrics, but by the end of the season, on the ball errors had almost become non-existent. Over the season as a whole, only Arsenal and Newcastle made more errors than Liverpool’s 36, however if you split the season in half, over the first 19 games Liverpool made 28 errors, over the last 19 games it was only 8. As an on the ball error will often leave the rest of the defence wrong footed, these types of errors tend to have a high conversion rate, and Liverpool conceded 10 goals from the 36 errors they made. If Liverpool can continue to keep the error rate at the level of the 2nd half of the season, then there would be a lot less hair pulled out by the fans this coming season. 

Perhaps it was the tough start Liverpool had, perhaps it was the getting used to Brendan Rodgers system, or perhaps they were just unlucky (probably a combination of all 3), but clearly Liverpool started the season really badly last year. If they can perform defensively as well as they did over the last 30 or so games, they could well turn a few of those losses and score draws into wins, and have a good crack at finishing in the top 4.

I posted this oginally on EPLIndex  http://eplindex.com/37116/defending-liverpools-defence-statistical-analysis-1213.html