Heracles and the failed art of shot blocking

One team in particular has been playing really out of sorts in the Eredivisie so far. Poor little Heracles have noted a 1-0-8 record to open the season with, and therefore occupy the bottom spot of the table with a poor three points from nine played. Still, in analytics terms, something makes them very interesting, and I believe now is the time to share this observation, so that we can follow it over the coming months.

HeraclesIf you’re not a die-hard Eredivisie fan, you may not know all that much about Heracles, so let me tell you something about them. They are a genuinely small team with strong local support, but as a well-run business they’ve been a stable Eredivisie side for ten years now. With your classical education background, you’d probably already noted their cool name, referring to a divine hero in ancient Greek mythology. Our hero was noted for his extreme strength and courage, something that can hardly be more out of place in reference the performance of Heracles the football team this season.

As a first exploration for that disastrous 1-0-8 season opening, I’d probably look at some shot numbers, expressed as per match.

Shots for                             11.6

Shots against                     11.9

TSR                                       0.494

Well, that’s weird. Apparently Heracles have a near balance in shots created and conceded, yet noted that 1-0-8 record. Bad luck, or something to do with shot quality?

ExpG per shot for             0.100

ExpG against                     0.125

Mmm, that’s an ugly picture. Heracles create shots that fly in in a 1 in 10 rate, and concede shots that usually convert at 1 in 8 rate. For years they have been fooling TSR with this behaviour, leading to overestimation of their strength in a metric that values each shot equally. This means that simply combining shots numbers and shot quality should provide part of the answer already.

ExpG created                    1.16

ExpG conceded                1.48

ExpG-ratio                         0.439

So, this metric should do it. But wait, 0.439 isn’t good at all, but it is far from in line with that 1-0-8 record. Usually, 0.439 teams record around 1.2 points per game, so something like a 3-2-4 record would be a more fitting reward for their play in terms of ExpG-ratio.

The answer, unsurprisingly for readers who remembered the title of this piece, lies in shot blocking.

Shots blocked offense                                 27.6%

Shots blocked defence                                10.2%

ExpG of unblocked shots created              0.91

ExpG if unblocked shots conceded           1.36

ExpG-ratio unblocked                                  0.401

An average 2014-15 Eredivisie team blocks around 19.2% of shots. Heracles’ offense sees around 50% more of its shots being blocked by opposing defenders. In return, Heracles’ defence blocks shots at a rate that’s 50% lower than their rival teams do. Their unblocked ExpG-ratio is a poor 0.401, which, combined with some tough luck goes a long way explaining the horrendous season so far.

Make of it what you will. Heracles might be very poorly organised from a tactical standpoint, producing below-average quality shots that are 50% more likely to get blocked, while the reverse is true for their offense. Those are some painful numbers that illustrate aspects that TSR won’t grasp.

But it’s a bit more nuanced than that. Using TSR to explain what has happened is always going to lose to measures like ExpG-R that take in more variables to correct for relevant details of performance, like shot quality, and in the case of unblocked ExpG-R also block rates. But do those aspects carry over from historic data to future performance, or are they mere variations that tend to even themselves out over time?

For comparison of the repeatability of TSR and ExpG-R, I’d refer you to earlier work on this site.

For the art of shot blocking, I haven’t shown data before. Here’s a simple scatter plot of the percentage of blocked shots that teams have noted in two consecutive seasons. That dataset here is EPL, La Liga, Bundesliga, Serie A, Ligue 1, MLS and Brazil 2012-13 and 2013-14.

Block rate - defensive - all shots Block rate - offensive - all shotsA few things are of note there.

  1. The relation between the rate of shots blocked in consecutive seasons is not particularly strong, so most of it is probably variance.
  2. Teams don’t note a shot blocking rate below 16%. This mean Heracles are either going to set a world record of poor shot blocking, or they’ll get picked up by regression any time soon.
  3. The defensive aspect of shot blocking is a tiny bit more repeatable than the offensive side, i.e. avoiding your shot getting blocked.

The problem with this raw analysis of blocks is that not all shots are blocked at the same rate. Here are the block rates for different types of play in the Eredivisie (2013-14 and 2014-15 data).

Direct FK                             24.7%

Open play shots               23.5%

First time attempt            22.6%

Rebounds                          22.3%

Indirect FK                         18.9%

Corners                             17.6%

Open play headers          6.5%

The distribution of shots from various situations may be different from team to team, thereby producing bias in the block rates. Some teams may concede more headers than others, which would make them look like poor shot blockers, since headers are rarely blocked.

To cut a long story short, of all different types of play mentioned above, only open play shots show any degree of repeatability in terms of the block rate. For all other types of play the correlation from season to season is virtually non-existent.

Block rate - defensive - open play shots only Block rate - offensive - open play shots onlyInterestingly, the R2 values decrease sharply when doing the repeatability test for block rates, leaving only open play shots to show any degree of repeatability, however small.

The fact that the overall analysis shows a stronger correlation than the subgroup analysis suggests that a big part of the repeatability of block rates of overall shots should in fact be bias introduced by the fact that teams show different distributions in types of play, rather than in actual block rate.

 

In the end

What can we, and our poor Heracles, take from this in-depth analysis of block rates? Simply put, they should be ignored when trying to predict future performances. The repeatability of block rates is very poor in general, and even smaller when performing subgroup analysis for different types of play. If your team suffers from very poor block rates, like Heracles, chances are this will regresses. Whether this means that block rates represent luck that events itself out, or managers that identify issues they get fixed rather efficiently, is impossible to tell at this moment.

For Heracles, this brings a touch of optimism, as their horrible block rates, both offensively and defensively, are expected to regress over the season. This should bring their actual outcome closer to their ExpG-ratio of 0.439, for a 1.2 points-per-game pace. This still means they are expected to add just 30 points (25 games to be played * 1.2 PPG) to their current total of 3 points. Relegation territory, but they should be within touch of the pack, rather than trailing miserably as they do now. We will be watching closely!

3 thoughts on “Heracles and the failed art of shot blocking

  1. Nils Mackay

    Is it possible Heracles’ block rate is so low is because they are very poor at defending crosses/other high balls, thus conceding a relative high number of headers in comparison to other teams? That could lower their block rate significantly I would expect.

    Reply
  2. John

    In your ExpG pieces of last week you said: “For this model I’ve [..] filtered out blocked shots (since shot blocking is a skill)”

    Are you gonna change that now?

    Reply
    1. 11tegen11 Post author

      Yes, I will.

      As a matter of fact, this piece started out as some background research to check if I should leave blocked shots out of the predictive model, or change it so to include them.
      It turns out the latter does best in predictive terms, so from now on blocked shots are in. I had based that assumption on earlier work elsewhere, but it turns out these finding probably weren’t correct, or at least not of significant influence.

      Well spotted!

      Reply

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