Mayo Kickouts (pre 2020 Final)

Kickouts are a huge element of any game. Where keepers kick them to, who gains possession, and how, and what teams do with those possessions. Prime example being last year when Dublin outkicked the Kerry press by picking out Brian Howard on the sideline and then setting up McCaffrey’s goal.

The interest in kickouts (as with most things!) tends to peak when Mayo are involved. The overarching sense in the build-up to this final is that David Clarke’s kickouts can be “gotten at”. So in the best traditions of the blog let us preview the kickout battle on Saturday by reviewing what has happened previously between these two teams.

Overview

We have six Championship games in the database between Dublin & Mayo (2015 drawn semi-final and replay, 2016 drawn final and replay, 2017 final and the 2019 semi-final) and whilst the protagonists, be that the Mayo keeper (more on that anon), the managers or the outfield players, have changed the overall outline of the teams, and the game plans therein, have remained intact.

In the six games there were 259 kickouts with Dublin gaining 13 (136 v 123) more possessions*.

*Personally I think language here is important; “winning” is a very positive phrase indicating that teams have done something “right”. But you can “win” a kickout that was terrible (opposition has a 3 on 1 but the one player comes out with the ball). Or indeed by not doing anything at all (ball dribbles out over the sideline). Instead teams gain, or retain, a possession.

Mayo defended those Dublin possessions well – to the extent that despite having 13 more possessions Dublin only produced 3 more shots. Mayo were more efficient at moving their kickout possessions to a shot. But as ever Dublin’s more clinical finishing saw them convert those three extra shots to 2-02 on the scoreboard.

However from a macro view, roughly 0 – 01, and half a shot, extra per game is nowhere near the dominance that is generally attributed to Dublin’s kickout performances.

Dublin’s kickouts

The above table compares Dublin’s kickouts in the six games against Mayo with opponents in other semi-final and finals during the same period. So we are comparing Mayo’s efforts with very similar calibre teams in similar high profile games.

And in truth Mayo have performed well by comparison. They have restricted Dublin to a 79% retention rate whereas other opponents have allowed Dublin an 84% retention rate. Now part of that could be the likes of Tyrone dropping off, and allowing Dublin to have the short ones, but when we look at what Dublin did with those possessions we can see that Mayo restricted Dublin to 0.33 points per possession (ppp) whilst others allowed 0.35. Mayo also scored more, both actually and per possession, than others did on the Dublin kickouts they gained possession of.

Combining all that Mayo come out looking better than Dublin’s other opponents allowing them to net 0.19ppp vs 0.23ppp against other teams.

That is one comparison point – to Mayo’s peers. But how does that 0.19 ppp allowed compare directly to Dublin?

Mayo kickouts

Things are not so rosy. Yes Mayo stack up well when compared to their peers however Dublin are a rung above and it shows here. Mayo restrict Dublin to 0.19 ppp on their kickouts … but Dublin have held Mayo to 0.12ppp on theirs. When they get their hands on the ball Mayo are more or less in line with Dublin scoring 2 – 26, or 0.33 ppp on their own kickouts with Dublin scoring 4-23, or 0.35ppp, on theirs.

The issue, in the main, is the sheer volume of Mayo kickouts that Dublin win. Dublin have only allowed Mayo to retain possession on 28% of Mayo’s kickouts. For comparison Mayo retain possession on 82% of their kickouts against other teams. Dublin retain possession on 79% of their kickouts versus Mayo.

On top of that volume Dublin look to strike hard. When they do get their hands on a Mayo kickout they produce a shot 62% of the time; others produce a shot 53% of the time they “win” a Mayo kickout.

Mayo keepers

One point of distinction within Mayo’s six games is the fact that they have used two keepers. The six games break down as two for David Clarke, three for Rob Hennelly with the sixth split more or less evenly when Clarke replaced Hennelly after 41 minutes of the 2016 final replay

Now not everything is based purely on the keeper. To absolutely, correctly, compare the two keepers we should be overlaying games state, the opposition’s tactics, the positioning of the outfield players etc.

But we don’t have that. Let alone that I only started recording the length of kickouts from 2017 onwards so we can’t, from this vantage, tell if Clarke’s higher retention rate is due to a higher proportion of short kickouts. Instead what we have is as per previous tables – retention rates and scores from kickouts won/loss. And against Dublin Clarke comes out well on top.

2020

So if Clarke has done well against Dublin, despite obvious failings such as the ball over the sideline at the end of the 2017 final, how is he, and Mayo, doing in 2020?

The answer is – not great. In their last three games (against Roscommon, Galway & Tipperary) Mayo have only retained possession on 69% of their own kickouts. We have seen that this was 72% previously vs Dublin … but acknowledge that Dublin is a step ahead of the rest. If the trend were to continue you would expect Mayo to come up with …. ~65% in the final??

And the more we delve into the numbers the less appetising they become. That retention rate of 69% includes short kickouts which account for 42% of all of Mayo’s kickouts. Mayo retained possession on 93% of these, which in itself is poor, but all kickouts out past the 45 saw Mayo with a Retention rate of 53% and a net ppp of -0.13. The opposition combined has scored 1 – 04 from the 18 possession gained off Mayo kickouts out past the 45. Mayo have scored 0 – 03 from 20 similar kickouts “won”.

Looking at the chart for these kickouts out past the 45 (we are missing some whereby the cameras didn’t pan out quick enough to see where they landed) we can see some obvious trends. Mayo have avoided the “mid-mid” section out to about 55m which ostensibly means that teams shouldn’t be able to come straight through on goal from one broken kickout however Clarke’s lack of relative length (think of where Patton or Beggan can land them) means that the whole “mid” section between the 45 & 65 can be flooded and the opposition are able to key on anything that is not pinpoint. Or has any shape of an arc on it.

Mayo have historically done better than others against Dublin on kickouts. Not better than Dublin but better than their peers. Clarke’s kickouts have had much better outcomes than Hennelly’s. But the 2020 trend is not encouraging and whilst in a one off game one or two breaks can have a big effect on numbers Mayo will, collectively, most definitely have to come up with something much better than has been the case so far this year.

Free Taking Review

This piece started out as a simple question – who is the best free taker? Just like reviewing the impact of short kickouts however that simple question opens up differing layers of complexities. So before we can answer that original simple question we need to disentangle the complexity, by understanding the various components, and then put it all back together again. (see note1)

Historic returns
Frees (for this piece 45s are considered free kicks) have produced a relatively stable return for the past number for years though there was a step up in 2017 that has been maintained.

The numbers involved, at just under 2.5k attempts, are robust. We can be happy that the returns, whilst covering approximately one third of all Championship games, are indeed indicative of free taking in the game at the highest level.

2017 – 2019
The relative stability of the returns means that the Expt Pts methodology works well. Thus we are able to look past pure Conversion Rates and take into account the relative difficulty of various frees to see who is outperforming the average

The above table shows the returns for any player with >25 attempts recorded in Championship games from 2017. Why we chose 2017 will become apparent. We can immediately see the importance of overlaying something (in this instance Expt Pts) onto the Conversion Rate through the lens of Rory Beggan. His Conversion Rate is low as the vast majority of his attempts are from distance. But his strike rate on these more difficult attempts is such that it ensures his returns, through Expt Pts, are well above average.

But this table is incomplete. It only includes those games fully charted and in the database.

To ensure a complete picture all frees from games not in the database, for those nine players, as well as the data for another six free takers from Roscommon & Tyrone (see note2), was reviewed and backfilled

Some quick highlights
• Diarmuid Murtagh comes in with a bullet; first on Conversion Rate and an above average return
• A “big three” emerges with Dean Rock, Seán O’Shea and Beggan pulling away from the others in terms of Expt Pts
• Through this lens Conor McManus & Michael Murphy are surprisingly low on the table
• Tyrone’s issues from deadballs are evident. They have used Seán Cavanagh & Darren McCurry as well but their main strikers have been below average as a trio

Shot Charts

From 2017 onwards I started to chart exactly where point attempts were taken from as opposed to just the sector which feeds into the Expt Pts calculation. This allows us to produce individual shot maps. It has also allowed us to create zones for frees – demarcated by the blue line above- with the guideline returns being 90% for “inside” and 50% for “outside”

In this guise below is Beggan’s shot map since 2017 (yes that is the 65m coming into view!)

He immediately presents a problem (one that we only really see elsewhere with Paul Broderick from Carlow) in that his “outside” shooting is very outside. Given that all attempts from beyond the 45 are placed into three zones based on the width of the pitch no consideration is really given to angle or length. And we can’t really model the returns as there are so few to compare against. It is therefore possible (probable?) that Expt Pts is underselling his performance.

To overcome this I’ve layered another zone so that instead of just “inside” and “outside” we now have “inside”, ”outside” and “ultra” (open to better names!!!). The “inside”/”outside” demarcation was subjectively arrived at after reviewing games. The below “ultra” outline is similarly subjective in nature. It could be moved 1 metre either way. Two metres perhaps? But looking at Beggan & Broderick’s shot maps we do have to introduce the concept so the below is as good an educated guess as any as to where to apply the line.

Individual players
Using these three ranges we now have new averages – 90% for inside, 58% for outside and 34% for ultra. Using these new ranges gives us a very different view of some players

Dean Rock

Mister consistency. Rock is second on the Conversion Rate ranking and first on Expt Pts. He is not padding his stats with short range efforts either. 71% of his attempts are “inside” and, whilst above average, his returns at 93% from this zone are not earth shattering. There are plenty with similar returns. What he is very good at is knowing his range – only two from “ultra”, which were out wide rather than long, with a lot of the “outside” attempts being centrally around the 45. This all aids his excellent 78% from “outside”.

On top of the mere accuracy we must note that 38% of his attempts have come in All Ireland finals and semi-finals. Preliminary work (here) has shown that game state can have an impact on a free taker’s returns. Given the sheer number of big game he has been in it is even more impressive that he has maintained this level of consistency.

Rory Beggan

No. 1 on “outside” shooting, from those with any volume, plus maintaining an above average return on the “ultra” length (40% versus a 32% average without his attempts included). The idea of a wrong side (right footer from the right) dissipates when we are shooting from such long range distances as the narrowness of the angle of the goalposts lessens, however it is still evident in Beggan’s chart. He is deadly centrally (80%; 0-16 rom 20) but if we draw a line up from the edge of the D he is 42% (0-07 from 17) on the right and 55% (0-06 from 11) on the left.

But that is nit picking. His abilities – such length whilst maintaining above average accuracy – is unique and is a deadly weapon. Quite apart from his accuracy just the threat of him means where you foul Monaghan has to come into any opposition team talk – which gives their inside forwards that extra split second to make their runs.

Conor McManus

We can’t talk about Beggan without reviewing Conor McManus. His returns were those that surprised me the most as my go to image of him is always hitting monster points from ridiculous angles. But that image hides a lack of consistency on his free taking; 84% “inside” is poor when you consider the volume he has taken from there.

Now there are mitigating factors. There are five misses from the right (wrong side) which indicates a lack of a reliable left footer whilst the subjective placement of the “inside” line goes against him. A metre further in and five of his misses would transfer from “inside” to “outside”. On top of the five misses inside from the right there are another two “outside”.

There are also another ten misses around the 45 which, when we compare to Beggan’s success there, make no sense as to why he would be taking them. They are obviously outside his zone. But the majority of these were pre Beggan becoming who he is. Eight of those ten central “outside” misses came in the 2017 campaign. Beggan had 12 attempts in total that year. McManus has more or less relinquished those efforts from which he is weakest from.

Seán O’Shea

The GAA’s own holy trinity is completed by Seán O’Shea. His unerring accuracy “inside” gives him a path to overtaking Rock on Conversion Rates but he will want to tidy up the “outside” shooting, especially just to the right of centre, to completely pull away from the other two in Expt Pts

Paul Broderick

Perhaps the most surprising entrant; he is the only member on the list outside of perennial Division1 & 2 teams. That in itself is a testament to the volume of games Carlow have played on their recent journey as well as their willingness to let him have an attempt given his accuracy.

Comments will become repetitive as we go through the players. He knows his limitations so doesn’t take them from the wrong side. The fact that he is left footed helps as “inside” right footed free takers are easier to come by. Again when the angle widens we can see him try some “ultra” attempts from the left. Prone to lapses of concentration – two of his three misses inside are within 25m on his good side.

Diarmuid Murtagh

His accuracy is good but he has been aided by the majority of his attempts being close and central. Again like Broderick the fact he is a left footer helps as Conor Cox & Ciarán Murtagh were able, in the main, to cover his “wrong side”

Neil Flynn

Neil Flynn took over from Kevin Feely as the main free taker in 2018. Overall conversion rate looks low at 71% but as the chart above shows he is reliable with his only two misses “inside” being from (a) the wrong side and (b) a straight on attempt close to the outside range. Has a weakness from centre to right of D for up to ~10metres outside the 45.

Peter Harte

As a unit Tyrone have struggled but Harte has held his own. Again like Diarmuid Murtagh he is aided by the fact that he is, in the main, an “inside” left footed free taker so does not have to take attempts from the wrong side. Again like Murtagh the majority of his attempts are within a specific close in zone – struggles on the edge of the “Inside” zone.

Shane Walsh

The only man to take frees off both feet (white = left foot above, black = right) and it is a testament to his two footedness that he is up there. The only other player I came across with this level of two footedness was Kevin Feely. Walsh was 94% (0-16 from 17) off his left “inside” and 85% (0-17 from 20) off his right. A bit of inconsistency with close in misses whilst Galway could develop a wide/long range shooter to take some of the pressure off as he struggles wide outside

Ciaran Murtagh, Conor Cox & Conor McAliskey

Murtagh & McAliskey had four attempts between them in 2019 whilst Cox’s volume is too small to extract too much from. All three come out with average returns on Expt Pts. Once again a reminder that average here is not bad – this is average in the context of the best players in our game in possibly the era with most collective accuracy ever.

Niall Morgan

Niall Morgan has an obvious comfort zone right of centre around the 45 (81%; 0 – 09 from 11). Everywhere else he has struggles (15%; 0-02 from 13). Playing amateur psychologist Tyrone see these as shots to nothing but there are consequences to such low returns – are they affecting Morgan’s confidence? Could Tyrone have scored more than 0 – 02 if they had gone quick with these attempts? Low volumes but poor enough game management.

Michael Murphy

Michael Murphy’s overall Conversion Rate at 66% is low but the perception, or mine at the very least, is that this was ok given the volume of his long range efforts. To some extent this is true as just under half (36 of 74) of his attempts come from “inside”.

And he is as good as the next man from “inside”. But this perception hides the fact that his long range shooting has not been up to scratch. He is, or at least has shown in Championship, that wide left, when he has, from distance, to swing them in, does not suit him.

For “outside” his returns of 45% are well below the average of 57%. Even more so when compared to those of the holy trinity – Rock, Beggan & O’Shea combined for 71%; 0-55 from 77.

Cillian O’Connor

In many ways Cillian O’Connor is the hardest player to peg. And the most surprising. I wrote on this subject, in what feels like a different lifetime, back in 2016 when O’Connor came out on top. He was the Dean Rock of 2013 – 2016. But this portion of his game has obviously regressed from there.

His “inside” shooting is still as metronomic as ever with his only misses coming from the right (due to the fact that Mayo have been unable to rely on a left footed free taker) and two out wide right on the subjective partition line.

The original “inside”/”outside” demarcation was not built on O’Connor’s data but it could well have been. The minute he steps outside that range his returns begin to plummet, from 94% “inside” to 41% “outside. This is the biggest drop of any player with any volume. The cherry on top being that he has missed all six from the “ultra” range.

Mayo have to help O’Connor here. Develop a left footed free taker. Transfer those longer range attempts to someone else. His brother perhaps? If you can’t do either start going short. Do something because what has happened for the last three years hasn’t worked.

Appendix

Note1: We must also understand that there are elements not captured. The impact of weather. The differing grounds. Pressure. Taking a free one point behind in an All-Ireland final is very different than taking one when ten points up in a Leinster final. We know all of these have an impact but that impact is not measured here.
We must also always be cognoscente of the small volumes involved

Note2; Roscommon & Tyrone were chosen given their relative success in the timeframe. We had the main free takers for the other “big” teams. Both of these had made the Super8s in both years

Short Kickouts Overview

Short kickouts. The bane of every traditionalist and subject of more opprobrium than steps, refereeing inconsistencies and red card appeals. Yet their prevalence continues to grow increasing from 46.7% of all kickouts in 2017 to 50.0% in 2019. That equates to a quarter of all possessions in a match.

So why, given that opprobrium, do teams persist with them? And do they work?

 

Why go short?

Short kickouts give a team possession (see Note1). And possession has been King of late with the average number of team possessions during a match dropping from 99.4 a game in 2015 to 90.3 in 2019. A reduction of ~9% over the past five years

The points per possession (ppp) by various kickout lengths also show that short kickouts far outstrip the returns for kickouts past the 45 (see Note2)

 

Short kickouts guarantee you possession in a game where possession is rarer; deny the opposition a scoring opportunity by kicking to a contest further out the pitch, whilst also being very productive compared to other kickout types. Why wouldn’t you go short?

Because that narrative is too simplistic. Incredibly so. A high proportion of short kickouts are conceded by opposing teams either through physical restraints (we know that no team can constantly press on the kickout) or for tactical reasons. Therefore basing efficiencies on whether a team gathered possession is too limited. It is what happens post gathering the kickout that determines how effective the routine has been.

(The best case scenario would be to know what percentage of possessions, emanating from short kickouts, were gathered versus differing defensive set ups. If a team defending the kickout drops you would expect the attacking team to take the easy option and roll a short one out every time. But it should be harder to score. If teams press up the proportion of short kickouts will drop as teams look to outkick the press. But we don’t have this granularity – see Note3 (more on what we do have later))

Here we look to introduce a new concept similar to the advantage that a server has in tennis (stay with me here!!). There is an excellent, if slightly out of date, article breaking down the nuances of the server’s advantage in tennis – https://www.tennisabstract.com/blog/2011/08/17/how-long-does-the-servers-advantage-last/ – but to quote that article

“… at some stage in the rally, the server’s advantage has disappeared. Four or five strokes in, the server may still be benefiting from an off-balance return. But by ten strokes, one would assume that the rally is neutral-that the advantage conferred by serving has evaporated”

The same rationale can be applied to possessions in football. At some stage the advantage of having a possession from a kickout, such as the ability to bypass a defensive shield or use a pre-set move, disappears. The type of possession moves from a kickout into what one might dub a transition phase and any scores from this (transition) phase should be removed from kickout returns.

How do we determine that inflection point? That point when a possession moves from “kickout” to “transition”? One way would be time – how long a possession lasts. Another is how many passes are in a possession (see Note4)? And it is the latter that we are going to use here. I have collected the number of individual player possessions, within a team possession, for a number of years now which we can use as a proxy for passes. The breakdown of player possessions per short kickout gathered are shown below

 

Half of all short kickout possessions end after six individual player possessions. How they ended (shot, turnover, end of half etc.) is not a consideration at the moment. That will come later. The tennis paper had a range over which the advantage of the server dissipates. Following that example I have created two kickout phases – “quick” possessions where there have been 1-6 player possessions and then “medium” for 7-11 player possessions. Anything with 12+ player possessions is deemed to have moved to the transition phase – any outcomes, given how long the defending team has had to set up, cannot be attributed to the kickout (see Note5). All scores emanating from short kickouts will be attributed to these three components – quick, medium and transition. It is up to the reader whether they want to put the middle portion – “medium” – into the kickout numbers or the transition phase. I have placed them within the kickout phase.

 

Short kickouts

So what happens on short kickouts? The team taking the kickout gathers possession 94.1% of the time. We will parse those below but for now, and the remainder of this piece, we will treat the 5.9% of short kickouts where possession was lost as one homogenous group. On these the opposition scored 0.67 points per possession (ppp)

That led to a shot

The first demarcation on short kickouts is whether the possession was progressed to a shot. In our sample (96 Championship games from ’17 to ’19 and in excess of 2,200 short kickouts) this happened 58% of the time.

There are five outcomes from any shot; a score, the ball goes wide, the ball drops short and the opposition gains control, the half ends, or the shooting team regains possession (blocked shot regathered, or the ball goes out for a 45). The breakdown, for these five outcomes, are laid out below

 

With a score, a wide, the end of the half or the shot regained the kickout possession has definitively ended. We can take the outcomes from these kickouts and determine our points per possession (ppp) metric. The question then becomes what we do with the 11% of turnovers within “quick” & “medium” (we already have determined that “transition” possessions are no longer considered within kickout returns). Should any scores off these turnovers be netted against the scores from the short kickouts?

My instinctive answer to this is yes – but only where the score is off a quick counter attack i.e. a shot is produced, after a kickout, with one to eleven player possessions (“quick” & “medium”), the shot drops short, or is blocked, and the original defending team now scores off one to six player possessions (“quick”). Why only “quick”? It is undoubtedly a subjective viewpoint but in the main with player possessions of 1-11 the original attacking team should be more or less set defensively. When the ball is turned over the impact on the defensive shape, from the kickout, will be very quickly remedied.

If we do this the ppp table for shots from a short kickout looks like this

 

No shot

To understand the effectiveness of any kickout strategy you have to take account of the scores the opposition managed to attain from the possessions they gathered. We have produced these numbers for (a) where the kickout team lost the kickout and (b) where the kickout team managed a shot from the subsequent possession. What of the instances where the kickout team didn’t manage a shot?

Again there are four main outcomes here; there was no shot because the ball went out for a kickout (without a shot), the half ended or the ball was turned over either inside, or outside, the opposition’s 45

 

Again “transition” possessions, where the kickout team had the ball for 12+ individual player possessions before losing the ball, are discarded. Possessions that ended at half time or where the ball trickled out over the end line without a shot are also discarded.

So the question becomes which of the turnover possessions, and scores from same, we take account of? A bit of subjectivity is required again. Personally I believe all turnovers, off short kickouts, where the ball was picked up outside the defending team’s 45 should still be considered as an extension of the kickout possession. And all scores off those turnovers should be netted against the short kickout outcomes (these are denoted in green above; see Note6).

For those where the turnover was picked up inside the defending teams’ 45 (denoted in orange) we have already set the rules – only those where the then defending team scores on a quick counter are considered. Our ppp outcome for these now looks like

 

We have now calculated all the component parts; a breakdown of kickout percentages, how long teams held onto the ball for after gaining possession, how many possessions were progressed to a shot, how many were turned over, what score came off all our various components.

A team takes 200 kickouts with the returns from same following the outcomes outlined above. What happens?

 

The kickout team ends up scoring 0-57 whilst the defending team scores 0-26 … a net 0.156ppp in the kickout teams’ favour. Still good. Still positive. But much closer to the net ppp in the original table above for mid-range kickouts.

By team

Using the new methodology how do individual teams perform on short kickouts? Unsurprisingly Dublin are way out in front aided by a double positive – they have gathered possession more often than anyone else (98.4% of the time) and are also just more clinical on their shots in general.

 

Tyrone do well here, in the main, as they are very good at getting the short ones away. They are second on the list gathering possession 97.9% of the time.

Kerry and Mayo hurt themselves here somewhat. The opposition has gathered possession 9.1% (Kerry) and 8.1% of the time – compared to 1.6% & 2.1% for Dublin and Tyrone – leading to them leaking a combined 0-15 here. This compares to the gold standard of Dublin; on their 251 short kickouts they have only ever given up 0-02 from directly losing the possession and one of them was in the 68^th minute when they were 9 points ahead against Galway in the 2018 AI semi-final (the other was Donegal in the 2018 Super8 game)

 

 

Note1: when talking about kickouts the term “won” is commonly used – “a team won 50% of its own kickouts”. I use the term myself; I interchange “won” & “gained possession”. But I shouldn’t. We need to move away from viewing kickouts as being won or lost as “won” indicates that the kickout was positive. But a team can win a kickout fortuitously (keeper kicks to a 1-v-3 for the opposition … but they all clatter into each other leaving his one player with the ball. This is a positive outcome on a terrible kickout). We need to think of kickouts in terms of gaining/losing possession as opposed to being won or lost.

Note2: this table is very simplistic. It nets, by length, the points scored on kickouts where possession was gathered versus points scored on kickouts where possession was lost.

Note3; partly due to time constraints on my part. But mostly due to the camera work of the various TV operators. Quite apart from the fact that one camera view is used for the vast majority of the game, so defensive shape cannot be determined, the operators tend to miss a number of kickouts due to the speed with which keepers get the ball away.

But even if we can’t see the exact defensive shape we can make some rudimentary notations as to how the ball was received. That was what I had started to do during the aborted 2020 league campaign. In the 11 games reviewed 72% of short kickouts were received uncontested thus further validating the need to go beyond whether the possession was gained to justify the tactic.

Note4; neither option – time nor number of passes – are fool proof. Any defending team can interrupt the possession by fouling and thus slow the possession down sufficiently to force it into the transition definition even if the time on the ball, or the number of passes, was relatively low.

Note5; these breaks are subjective. There is probably a statistical method available to split these groupings more accurately but my background is not academic enough to perform that. So we will, reluctantly, go with the gut feel based on the chart

Note6; we can tie ourselves in absolute knots here about what cohorts to include/exclude. But the volumes are small – 275 short kickouts ended up with the ball being turned over outside the opposition’s 45. Only 16 of these had 12+ player possessions. For simplicity’s sake they are all treated as one.

Updated Raw Expt Pts post 2018 Championship

The last published Expected Points (Expt Pts) numbers are contained here. I would strongly recommend that everyone, whether you are currently using Expt Pts or not, (re)read this piece as it outlines the methodology used and more importantly the inherent weaknesses in the numbers.

Changes since last publication

Those numbers were produced in early 2018 but had not baked in the 2017 returns … so in effect were created off Championship data up to and including the 2016 season. Some 70 Championship games have been added to the database since then so it is time to update the numbers.

A quick review of some changes

a) Firstly all attempts, inside Section 8, have been coded as being with the hand or the foot. This does not get anywhere near representing the quality of goal chances but, as can be seen in the differences for both, is a worthwhile change.

b) Secondly the raw averages have been dispensed with and instead a weighting is now in effect; 30% of total outcomes are taken from the two most recent years with 20% each for the two years preceding this. What is happening in more recent years is more prevalent in the Expt Pts output but we spread the return across the four most recent years to ensure any one year change/blip does not completely overwhelm the model.

c) This is the case except for rarer shots (sideline attempts, 45s, penalties etc.). For these we take all the shots in the database rather than applying the weighting. This gives us more certainty on the numbers.

Outcomes
Yes. Yes. Yes. We understand – just give us the bloody numbers …

Caveats
As ever Expt Pts is not the be all and end all. It is another tool to use when honing in on shooting efficacy. One tool (albeit a better one than Conversion Rates). No more, no less.

Consider these numbers as “Raw Expt Pts”. No overlay. No subjectivity. They are the product of the 80:20 rule. A newer model, which overlays strength of competition and game state, is currently in production. The thought behind this updated version is expanded upon here.

That will undoubtedly produce better numbers but it is not necessarily something that can be transferred easily to club football. As such the numbers here are better in that respect.

Happy statting.

2018 Division 1 Review

The 2018 league saw the continuation of Galway’s upward curve as well as perhaps a chink in the Dublin armour as they lost a regular league game for the first time since March 2015. As will be outlined below Galway played the league differently to everyone else and make an interesting hook when reviewing various metrics; they have thus been added to the recent “Big Four” when reviewing how individual teams perform.

A few of the metrics were introduced in the Week4 review (here) so this review can be seen as an extension of that piece now that we have more date.

Possessions
In boxing they say that styles make fights. In football team set ups and tactics make games. Dublin v Donegal, at 103 possessions during the game, had 26 (34%) more possessions than Dublin v Galway in the league final.

Below are the top and bottom five individual team possessions recorded throughout the 2018 league

Galway continues to play a different game to everyone else. Of the 36 individual team outings (two each per the 18 TV games) Galway’s volume of possessions only once came close to the average of 45.2 a game when they recorded 44 against Monaghan. Otherwise their remaining four fixtures all ranked in the bottom five in terms of possessions. Only in the aforementioned Monaghan game did they have more possessions than the opposition – in that instance two. In the other four games they lost the possession battle by 3, 6, 7 and 8 respectively.

Whilst the spread of possessions at 26 (max = 103, min = 77) would appear to be wide it is actually more condensed than the 2017 Championship when the spread was 34 (113 possessions in the Galway v Mayo game and 79 in the Carlow v Dublin game). Yep that’s the same Galway one competition and about six months removed. I am currently finding it very hard to reconcile the Galway of 2017, which was involved in games with 113 and 110 (QF v Galway) possessions, with that of the 2018 league where the possession count never topped 86!

As well team possessions we also have the number of successful passes within each possession. This can be used as a proxy for that dreaded word – transition.

Dublin’s method of continually probing whilst stretching teams wide has been readily commented upon and it shows up here. They own six of the 11 sequences where there was a minimum of 20 player touches. Those possessions and their outcomes are listed below.

After first producing this table I was asked if it was meaningful that only four of these possessions led to a score. I don’t believe so (a) as the volume is too small to make any concrete statements on and (b) the intention wasn’t always to score – some of these were teams playing keep ball to wind down the clock.

Whilst the above table is “interesting” it doesn’t provide any usable insight. That will come. For instance; once enough data is gathered we can see whether moving the ball through multiple players or the quick strike is more productive. Which teams play fast on the counter – and which teams do not. Until then – we’ll have to do with the “interesting” table!

Another way to use the possession data is to see where the possession originated from and overlay shot data to see how effective teams are depending on where the move starts

At a league wide level

– just under half of all possessions originate from kickouts (34% on your own 15% on the opposition’s)

– 46% come from turnovers (17% inside your own 20m line, 16% between the 20m and 45m lines with the rest picked up higher up the pitch outside your own 45)

– the remainder coming from restarts and shots gone awry (short, blocked and picked up, off the post etc.).

Just knowing that alone you can see why kickouts are such a focus. But should they be? Teams shoot as frequently on their turnovers as they do on their own kickouts. Despite, notionally, teams not being as set when they turn over the ball inside the opposition’s 20m line they allow shots less often than on short kickouts. Dropping the ball into the keeper’s hands is not the mortal sin we have been led to believe ….

But averages simplify the process completely. Some teams are better at transitioning from a kickout – others from turnovers. The below table shows the shots per possession, by where the possession originated from, during the 2018 league

Surprisingly Dublin didn’t excel anywhere and were (relatively) poor on their own kickout. Galway – as is necessary given their low possession game – were above average in all phases. A measure of their efficiency – they won four of the ten restarts and scored 0 – 04; they regained the ball 5 times from shots dropping short, coming off the post etc. – they scored 0 – 05.

Offensive Production

A few things that jump out

– Average Conversion Rate at 55.7% is a 3.3% increase on the 53.9% recorded during the 2017 Championship. There are many reasons as to why this might be but it is just worth noting for a rising ship should lift all boats.

– Dublin did not produce more shots than the opposition (Tyrone actually produced an extra 2.5 shots per game – small sample size alert – whilst only Mayo produced less shots per possession) but were head and shoulders above everyone else when it comes to shooting accuracy. Noteworthy, however, that this was just a continuation of their 2017 form when they recorded a 62.3% Conversion Rate.

– Galway have been very accurate but in a different way to Dublin. Dublin were 60% from play whereas Galway were 52%. But Galway had a significantly greater volume of their shots skewed towards the higher percentage deadballs (26% of Galway’s shots were from deadballs as against 18% of Dublin’s). If the frees dry up, or McHugh’s radar is off, can they generate enough shots from play to overcome their low possession and average Conversion Rates?

– Mayo struggled offensively throughout the league. Their shots per game and their accuracy were both well below the league average. They will be fervently hoping that come the Championship they will be back up to their 2017 levels when they produced 0.63 shots per possession, with a 54% Conversion Rate, across ten games.

Another aspect of attacking play is the frequency that teams go for goal. We all know that goals can inflict monumental damage no matter when they occur (Dublin in the 4th minute against Tyrone in the 2017 semi-final?) but knowing and doing are different things. Do teams go for goal at different rates?

Anyone surprised to see Dublin be so far ahead of the opposition on the frequency of their goal attempts? Me neither. Though I am somewhat surprised to see just how bunched the rest of the teams were. That gap, and bunching, was not evident during 2017 (below). The differing quality of opposition – as opposed to the league when everyone’s opponent is of a comparable standard – feed into the higher rates observed in 2017.

Player level

SHOOTING FROM PLAY

The average Conversion Rate for all attempts from play is 47%. There are reasons why a particular player’s results might vary from this (shooting more against better teams, close in shots versus those from the wings etc.) but it is a very useful yardstick. In that context Dublin’s trio of Kilkenny, Scully and Basquel are off the charts. I don’t care if all shots were taken against beaten dockets (they weren’t) or if there was absolutely no pressure (there was) – that is incredible shooting.

Considering he is Donegal’s main man, and has the added responsibility of taking the frees, McBrearty’s numbers are no less stellar. Defenders know he is getting the ball, they know he is shooting off his left (15 of the 16 point attempts were off his left), yet he still produces.

Comer’s returns look unusual in that he has a very high Conversion Rate but has below average Expt Pts. The simple explanation for this is his poor returns on goal attempts. He had four shots at goal across the five TV games scoring 0 – 03. This helps his Conversion Rate enormously (75%!) but harms his Expt Pts return as he’d be expected to score 1 – 02 from those four attempts.

DEADBALLS

It is a rare enough deadball table that shows Dean Rock comprehensively outplayed but Barry McHugh did just that during this league campaign. Brennan & Clifford also had better Conversion Rates than Rock but their Expt Pts mark was very similar to his showing that they converted slightly easier frees more often.

McHugh’s shooting was not only more accurate (90% Conversion Rate vs 83% for Rock) but also much better in terms of Expt Pts (+2.4 vs +1.1) indicating he converted much harder frees at the same, or a better, rate. Given the aforementioned lack of possessions Galway have a higher need to squeeze as much out of each one as possible. They did this throughout the league in no small part due to McHugh’s proficiency.

Mayo’s deadball woes were very evident throughout the whole campaign. As a team they were 69.5% (0 – 32 from 46) on deadballs leaving 0 – 05 behind them when compared to what the average Conversion Rates on those 46 attempts would be. This was very similar to the 2017 returns where they returned 69.4% (0 -50 from 72) and an Expt Pts mark of -5.36.

ASSISTS

We have started to introduce the idea of Expt Pts for assists and below is a plot for the 20 shooters listed above. It is important to note that for this metric the more games you play the higher your Expt Pts on assists will be as unlike Expt Pts for shooting there is no negative return. You assisted a shot; the outcome is irrelevant. A “per 70 minutes” metric would be much better and this is what will be produced during the 2018 Championship

That being said Fenton remains an absolute beast – he is no midfielder. Rather he is a master puppeteer centre half forward laying off ball to the shooters and/or converting at a ridiculous rate himself.

Despite the above notes on the volume impact we can see the affect Comer and Clifford had throughout the league. Their shooting was by no means stellar but their involvement in setting up teammates was excellent as measured by the impact of their assists. Comer’s direct running plays a part here – Galway took a shot directly from 11 possessions in which Comer was fouled, the next highest was 5. Granted there is huge discrepancy in the volume of minutes played but that is stark.

Defensive Production

Dublin allow more shots, on a per game and a per possession basis, than the other big teams which, when you consider their recent dominance and the fact they won the league is a remarkable thing. But even more remarkable is the poor Conversion Rate from Dublin’s opposition. The average is ~56%; Dublin’s opponents are at ~47% whilst no one else dips below 53%. Why would this be?

We have never been able to concretely attribute poor offensive numbers to either good defending or poor attacking. To date we have had to assume it is a mixture of both. But there are some obvious things we can look at when one teams’ numbers are so out of step with the norm.

Frees; Frees are converted much more readily than attempts from play. If the ratio of frees faced by Dublin is vastly different than that of other teams this would affect the overall Conversion Rate. It is different but not vastly; 22% of the shots faced by Dublin were frees as against 24% for everyone else. That equates to about 0.25 frees per game which isn’t really worth a whole lot in terms of Conversion Rate divergence. Dublin’s opposition converted frees at 73% – the league as a whole was 77%. Small gains but nothing earth shattering.

From play; So if it is not frees then it must be from play. The league average conversion rate on point attempts was 49% (the 47% mentioned earlier also includes goal attempts); Dublin only allowed 39%. That old chestnut – excellent Dublin defending or poor attacking? It is not strong Dublin defending per se – I chart the pressure applied to each shot and the Dublin defence applies “strong or severe” pressure to the shooter at a league average rate (44% for the league, 42% for Dublin). There is something in where Dublin’s opposition shoot from however; against Dublin 47% of the point attempts come from the wings between the 20m & 45m lines – the league average is 38% and if we remove Dublin that drops to 36% for the other six teams. So in a sense it is Dublin defending. We have seen that they allow more shots per game but they “let” you shoot from more disadvantageous regions – this would also feed into why their pressure % is not as high as expected.

Playing Dublin; But then again we have another overriding theme – the pressure of playing Dublin. When we restrict the pressure index to central shots only Dublin are relatively poor – only 31% of opponent’s shots centrally were taken under strong or severe pressure as against the league average of 44%. Low volumes but still! The kicker is that 53% of these central shots against Dublin were converted as against 63% for the rest of the league. We cannot place this performance on Dublin defending – indeed the opposite is true. The Dublin pressure is less intense. Teams missed the simplest of shots (centrally and under no pressure) at a higher clip.

Enough of Dublin! The conversion rate of Tyrone’s opponents is almost comically high. I double checked just to be sure. In Tyrone’s three games Dublin hit 68% of their shots, Monaghan 63% and Kildare 62%. It is only three games, and the comparable 2017 return was a combined 48% (five games) so I’m sure Mickey Harte and the backroom team are not overly concerned.

Kickouts

On the whole all teams are winning a lower percentage of their own kickouts when compared to the 2017 Championship campaign (73% won in 2017, 66% won in 2018) with none of the highlighted Division1 teams bucking this trend. Part of the reason for this is that the volume of short kickouts has dropped (a consequence of the new rule – either directly or indirectly as teams kicked longer in anticipation of the press that will surely come during the Summer) from 47% in the 2017 Championship to 40% in the 2018 League. Teams win their own short kickouts at a 94% clip so if there are significantly less of them the overall win rate will suffer. There was also a drop in the percentage of kickouts past the 45m line won by the kickout team – from 56.9% to 54.5%. Small enough but when you combine the two – a greater volume of longer kickouts with these longer ones won less frequently – we get a decent drop in the win rate.

Outside of the win rates it is interesting to see who is the most productive. Dublin are generally considered Kings of the kickout but in terms of net effectiveness they were only above average in this league campaign whilst Tyrone actually outperformed them in 2017. Mayo were very good on their own kickout during the league – they will be hoping that their overall Conversion Rate picks up so that they can build on this strong platform.

As is becoming a theme Galway was the outlier. Their net returns on kickouts are very low when compared to the other big guns – with one of the main reasons being that they continue to shun the short kickout. In their five TV games they went short on 27%. Mayo were 55%, Dublin were at 47% with Kerry and Tyrone at 41% apiece.

Volumes become low when we begin to segment like this so the percentages become less reliable however given that they are going short at a lower rate this allows the opposition to “tee off” on their longer ones. When they went past the 45m line Galway won 50% of their kickouts; the comparable figure was 65% for Dublin and 57% apiece for Kerry and Mayo.

Expected Points (Expt Pts); numbers to use

What is Expt Pts (Expected Points)?
Expt Pts is very loosely aligned to Expected Goals (xG) in soccer. You take every shot and assign it a value linked to the likelihood of its success based on the observed historical averages.

Essentially you track every shot and once you have enough data points you work out the average return for each shot. The average then becomes the “expected” and you are up and running (worked example and current outputs below)

Restrictions
Before we set out I am acutely aware that Expected Points is not the answer to anything. It is a tool to quickly highlight if there might be issues. In its current form it is very raw. We know from other sports that the game state is very important (teams chasing a lead in soccer tend to have more shots for instance) whilst the quality of opposition can also be a factor (football outsider’s DVOA v VOA). As such the values herein will be referenced as “Raw Expt Pts”. Other variables need to be overlaid onto the raw averages, whilst tighter sectors also need to be worked upon, to elevate it from its raw state.

These deficiencies are known and acknowledged. But as a quick reference point it is still a better guide to a teams’ returns than pure Conversion Rates – or worse still a count of the number of wides (don’t get me started!)

Working out the average
Unlike soccer those working in the GAA do not have a company, such as Opta, collating shot location data – it has to be done manually. The area on the pitch from where shots emanate from is also much greater.

Rob Carroll over at gaelicstats has produced a detailed return graph showing how often shots are converted from various pitch co-ordinations. The Expt Pts herein uses a more simplistic sectoral approach. Whilst Rob’s shot location chart is much more granular we will run into sample size issues were we to use something akin to it. Instead I started tracking shots using the below grid to (a) ensure I overcame any sample size issues and (b) it enabled me to use the pitch markings so as to be consistent with the shot location seeing as I was doing it manually off TV pictures (the picture quality of which can vary)

As well as shot location the type of shot is noted – 45, free, point attempt from play, sideline, penalty – as well as whether it was a point attempt or a shot on goal.

So we end up with three questions that need to be accounted for on every shot – location (which is then converted into one of the above segments), type and whether it is a goal attempt.

The type is fixed and whilst there is some subjectivity on the location generally speaking we have a lot of latitude given the larger segments we are working with. There is even less subjectivity on whether a player is going for goal though you will occasionally need to be a mind reader!

Expected Points
Fine. I have the above tracked for a game and realise its limitations – now what? Now you start to (a) produce data and (b) use it to review. Below is the Raw Expt Pts return for every combo mentioned above (pitch position, shot type and goal attempt or not).

Should you wish you can start to use these immediately as they are based on a dataset that comprises well in excess of 4,000 shots. That dataset comprises entirely of intercounty Championship games but I have used the outcomes on league & club games as a starting point and taken the results with this in mind.

Take a point attempt, from play, from sector 4. The Raw Expt Pts for every shot from this sector is 0.39. Every shot is thus expected to produce 0.39 points. Now we know the binary outcome for this shot is either a score (1) or not (0). Thus if the shot is converted the Expt Pts outcome is 1 (scored 1 point) minus 0.39 (the expected value) for a return of +0.61 (1 -0.39 = 0.61). If the shot is missed, for whatever reason, the Expt Pts outcome is -0.39 (0 – 0.39 = -0.39).

Do this for every shot using the appropriate “Raw Expt Pts” value. You can now add up an accurate, appropriately weighted, value for each shot.

Concrete example
Below is the Mayo shot chart from the 2017 All Ireland defeat to Dublin.

Mayo’s Raw Expt Pts come in at +1.84 which means that, at a macro level, they scored about two points more than they should have (were expected to) going on how often inter county teams historically converted those attempts. Their shooting was very good.

Breaking the shooting down we can see that the “very good” shooting was not universal. Two goals attempts returning 1 – 00 is just above what is expected. 21 point attempts returning 0 – 12 (57%) with a +2.39 Expt Pts is exceptional … but the excellent shooting was restricted to the central Sector 5 channel. Deadballs were poor.

By using the Expt Pts – with all its inherent flaws – you can quickly highlight areas that need addressing (or alternatively you can get bland results across the board and move on to something other than shooting). Its real power comes in over longer timeframes where the returns from multiple games can be reviewed.

Build upon the data and you can get a sense of the team’s relative strength/weaknesses/tendencies. Do it for the opposition and you start to build up a picture of the defence.

Expected Wins – 2016 season

In 2015 we introduced the idea of Expected Wins (“Expt Wins”) which reviews a team’s season, on a game by game basis, versus what they were expected to do using bookmaker’s odds. There are undoubtedly drawbacks to this which are expanded upon in the original article (here) however it is an interesting prism to view the season through rather than just looking at those that had a “good Championship”

2016 Top Performers

Team	Games played	Wins	Win %	Expt Wins	above/below Expt Pts	Win % rank	Expt Wins rank
Clare	14	9	64%	5.465	+3.535	5	1
Dublin	16	15	94%	12.218	+2.782	1	2
Longford	10	5	50%	3.430	+1.570	=11	3
Louth	11	7	64%	5.483	+1.517	6	4
Carlow	10	4	40%	2.750	+1.250	=18	5

As can be expected those teams that have a good winning percentage are also high up on the Expt Wins ranking. Keep winning and you’ll consistently outperform your odds and thus your Expected Wins. Dublin, despite their very short odds at times and thus very high Expt Win total, being the poster child for this.

The two teams that had the most unexpected run in the Championship – Clare and Tipperary – are both comfortably in the top10 (Tipperary are joint 6th with an Expt Win total of +0.889) as they made a mockery of their odds at times. Indeed Clare also had an excellent league campaign, winning five of their eight games and picking up the Division 3 title, which helped them climb to first in the table.

Longford had a lesser league campaign winning three of their seven games but completed one of the shocks of the Championship beating Monaghan away in the qualifiers at odds of 15/2 which accounted for nearly all their positive Expt Win total. Removing the bookmaker’s margin they were expected to win that game ~8% of the time.

Perhaps the biggest surprise is Carlow. They won three games in the league for the first time since 2012 (when there was an extra game as Kilkenny played boosting everyone’s win volume in Division4!) whilst also recording only their second Championship win since the end of the 2011 campaign. You won’t read about Carlow having a good season anywhere … but the fact they outperformed expectations to such an extent should ensure it is considered as such.

Versus the Handicap

Another way to review a team is through the prism of the handicap. Bookmakers will tell you that they set the handicap line according to what they think their customers will back, thus giving them an even book, rather than the respective merits of the teams. Public perception, along with the strengths of the team, thus feeds into the handicap line. Even still it is quite a good barometer of how teams are expected to perform. So how did teams fare against the handicap and also the two rankings above?

Team	Games played	Games covered	Cover %	Cover Rank	Win % rank	Expt Wins rank
Kerry	13	10	76.9%	1	=2	=6
Louth	11	8	72.7%	=2	6	4
Fermanagh	11	8	72.7%	=2	=20	11
Tyrone	13	8	61.5%	=4	=2	=8
Cavan	13	8	61.5%	=4	=10	9

Only Louth appear in the Top5 of both the Expt Win and Handicap rankings though in truth all 5 that covered the handicaps most often were also high up in the Expt Wins Ranking.

Fermanagh may have only won four of their 11 games in 2016 (two at home in the league, the home win versus Antrim in the Ulster Championship and then away to Wexford in the qualifiers) however they were a tough nut to crack with only 3 teams managing to beat them on the handicap (Cavan winning by 6 in Brewster Park, Derry in the opening round of the league and Donegal in the Ulster championship). Indeed that Cavan game was the only one of the five that Fermanagh played at home where they failed to cover the handicap. We saw something similar in 2015 when they covered in five of the six games played at home. Fortress Brewster!

But this all buries the lead. Kerry were excellent against the handicap (see Note1) where they only failed to cover in the opening two rounds of the league, against Dublin and Roscommon, and again in the league final. We have seen a phenomenon of double digit favourites tending not to cover (see Note2) but when a big favourite in 2016 (11 + 10 versus Clare in their two games and 9 versus Tipperary in the Munster final) Kerry covered each time. They don’t get dragged down by “lesser” opposition – they play to their own level.

2016 Worst Performers

Team	Games played	Wins	Win %	Expt Wins	above/below Expt Pts	Win % rank	Expt Wins rank
Cork	11	5	45%	6.617	-1.617	15	28
Limerick	10	1	10%	2.799	-1.799	31	29
Monaghan	14	4	36%	5.829	-1.829	=20	30
Down	9	0	0%	1.960	-1.960	32	31
Armagh	10	2	20%	3.963	-1.963	29	32

It is no surprise, given that league games can make up 60% to 70% of a poor team’s season that relegated teams feature heavily here. Between them Cork, Down, Armagh & Limerick mustered five wins from their 28 league games.

Down didn’t register a win in 2016 however Armagh, despite winning two games, came out slightly worse on Expt Wins. This is due to the fact that they were expected to be competitive in Division2. Outside of the Round6 game away to Tyrone, when the reality of their form was beginning to catch up with their odds, the largest price they were in any league game was 7/4. They also played three (see Note3) Championship games where they were given a good chance of winning each (2/1, 11/10 & 9/4) but came away with three losses.

Down on the other hand started slowly and continued to plummet. They may have lost all nine games but were only ever expected to win ~1.96 of those. Armagh were expected to win ~3.96 of theirs.

Monaghan had an average league campaign winning three of their seven games when they were expected to win ~3.1 games. Their issues were in the Championship where they had two 50:50 games with Donegal but won neither and then lost as 1/12 favourites at home to Longford. That loss alone equates to -0.893 Expt Wins.

Versus the handicap

Team	Games played	Games covered	Cover %	Cover Rank	Win % rank	Expt Wins rank
Laois	12	4	33.3%	=28	=24	=23
Derry	12	4	33.3%	=28	17	25
Waterford	9	3	33.3%	=28	28	27
Down	9	3	33.3%	=28	32	31
Wicklow	9	2	22.2%	32	=24	21

This is the “hope you weren’t relying on these guys throughout the year” listing.

Similar to the positive performers table there is only one team – Down – that appears in the worst list for both Expt Wins and handicap rankings. Again like the top performers those at the bottom of the handicap ranking are also in the bottom third of the Expt Wins ranking.

Waterford & Wicklow appear in the bottom 5 for the second year running. Combining 2015 & 2016 they are running at a 28% cover rate (9 games from 32).

We have already seen one of the teams relegated from Division2 – Armagh – appear in the “worst performing” category so it’s no surprise to see the other two teams that filled out the bottom three of that division make an appearance here. Division2 was seen as ultra-competitive at the start of the year and this was followed through with a high volume of games with a close handicap (see Note4). Once teams started to underperform they were going to struggle against the handicap.

Notes

Note1; what is more remarkable, and a cautionary tale re taking trends at face value, is that Kerry were in the bottom two in this metric in 2015. Then they covered in 31% (4 out of 13) of their games. We must always remember that we are dealing with very small sample sizes (and margins) here where a point or two can have a huge effect.

Note2; twenty teams since the start of the 2015 league campaign have started as double digit favourites. Only 7 (35%) have managed to cover this.

Note3; Although the first Laois game was declared null and void it was played as if it was a Championship game and we have odds & a result for it so it is included overall.

Note4; 86% (25 games out of 29) had a handicap of 2 or less in Division2. This was 52% for Division1, 83% for Division3 and 48% for Division4

Thinking out loud; In GAA betting the handicap is intrinsically linked to the match price. If those with high Expt Wins (essentially odds on shots) generally cover at a better rate, and the corollary is true for those with low Expt Wins are the linkages between match price and handicap line “out”?

Early Conversion Rates are poor – why?

Early Conversion Rates

Whilst uploading the 2016 data into the database I was noodling around in the numbers and produced a simple chart for production on Twitter.

Something was quite obviously happening in the first 10 minutes that saw the cumulative Conversion Rates much lower than the average. There were two initial thoughts

1. a number of “lower level” teams were dragging the average down early in games (either through just poor shooting or an inability to get “quality” shots off against better teams early on when the scoreboard was close)
2. shooting types, and where shots were being taken from, were so different in the frantic opening periods of games that the early Conversion Rates were being skewed

Upon doing some more superficial digging it appears that neither were the case

1. Conversion Rates by teams

The phenomenon (of Conversion Rates being lower early on) was observed in three of the four semi-finalists (NOTE1) whilst all other teams followed the overall trend to a tee. The only outliers – unsurprisingly – were Dublin.

2. Expected Points over time

The above is a replica of the Conversion Rate chart but replacing Conversion Rates with Expected Points (Expt Pts). Although the shape of the chart is different than the original the occurrence of poor early returns is still evident. And by using Expt Pts we remove the shooting types as an issue as Expt Pts bakes in the difficulty of a shot (NOTE2). All shots are being converted at a lower rate than expected until around the 30th minute but teams are noticeably struggling in the first 15 minutes.

Conversion Rates by shot type

So the phenomenon is real but cannot be attributed to a specific team type nor to shot selection/execution. It is across the board except for Dublin. Three shots types – free kicks, point & goal attempts from play – make up ~97% of all shots. Is there anything we can determine from investigating these shot types independently to explain this poor shooting in those early exchanges? And is there anything therein that explains how Dublin are managing to avoid this poor shooting early on?

Free kicks

This is probably the most surprising, and hardest to attribute, of all the results. When the very first chart was produced on Twitter I mischievously suggested that whatever all the back-up teams were doing to get teams warmed up they needed to change it. There were some good responses re the intensity of teams, especially in the pressure applied to shots, being higher early on. Or that teams were defensively more conservative early on leaving less space for clear shots. All plausible and probably have a grain of truth. However none applicable to free kicks – and the phenomenon of poor conversion rates early on is noticeable here too.

Now by slicing the volumes into the first 10 minutes of one season’s games we are running in to sample size issues. Specifically for this segment the volume is 47 so this comes with a rather large health warning.

Assuming games are now 80 minutes the first 10 minutes make up 12.5% of the game; the 47 frees in the first 10 minutes make up 13% of all frees. On top of that the two main free takers – D Rock & C O’Connor – make up 21% of all frees in the first 10 minutes whereas they make up 25% of all frees in the database for 2016. So the first 10 minutes, low sample size and all, are representative of the whole year. So what happens in those opening 10 minutes?

	Shots	Scores	Expt Pts	Conversion %	v Expt Pts
All frees	47	29	32.8	62%	-3.8
Rock & O’Connor	10	9	7.8	90%	+1.2
Others	37	20	25.0	54%	-5.0

What the above table shows is that Rock & O’Connor were on point from the get go. Overall for the year they combined for an 86% Conversion Rate and in the first 10 minutes they were 90%.

If the two main protagonists were on point the rest of the free takers must be dragging the averages down from 71% overall to 62% in the first 10 minutes. And as the table shows this is the case. Indeed they were very poor returning a paltry 54% (the 80 minute average for all free takers outside Rock & O’Connor was 66%).

And this somewhat negates the argument for lower Conversion Rates early on being affected by what the opposition’s defence is doing. The opposition can’t really affect free taking. Outside of Rock & O’Connor it looks like free takers were just not ready early on (NOTE3).

Points from play

The Conversion Rate for 2016 was 44.2% and for the five years from 2012 was 45.8%. For the first 10 minutes of 2016 games the conversion rate was 36% and only rose to a cumulative 38% by 20 minutes. Again the Expt Pts was lower in the first 10 minutes (-15.70) as against the remainder of the game (+5.84).

I do track whether a shot was taken under pressure however have only used it anecdotally to date as it is a simple “Y/N” flag and is probably not nuanced enough for any concrete use. Having said that however there is only one person applying the flag so we would expect a certain degree of consistency of application across the ~1,000 shots tracked here.

In the first 10 minutes I charted 53.6% of all point attempts occurring whilst under pressure. The remainder of the time it was 54.2%. Near enough as makes no difference.

So the poor shooting for points from play is real, is not linked to poorer shot types (as evidenced by the Expt Pts return) and from the empirical data we have is not linked to greater pressure applied earlier on in the game. I am completely open to the intensity of the pressure being different early on (NOTE4) but if this was the case you would expect some uptick early on in the percentage of shots marked as taken under some/any pressure in this timeframe. There is none.

There may be other non measurable factors such as nerves (these are amateurs after all) but as of now I can’t come up with anything other than the aforementioned “mischievous” reason that players are just not at peak performance early on. Maybe this is to be expected?

So what of Dublin? We saw that their early conversion Rates outperformed everyone else. This is in part due to the fact that Dean Rock went 5 from 5 on his frees but how was their shooting from play?

	Shots	Scores	Expt Pts	Conversion %	v Expt Pts
Dublin	23	9	10.2	39%	-1.2
Mayo	28	8	12.0	29%	-4.0
Tyrone	19	8	8.7	42%	-0.7
Donegal	17	4	7.4	24%	-3.4
Tipperary	13	5	6.8	38%	-1.8
All first 10	179	65	80.7	36%	-15.7

Again volumes are low (NOTE5) but Dublin were no great shakes early on. Yes they were above the average for the first 10 minutes but they still underperformed when compared to the whole game average and their Expt Pts – like all the teams above – was below 0.00.

Perhaps the most striking return here is Mayo. From the 10th minute onwards they were exactly in line with Dublin (Mayo 49% on 126 shots with an Expt Pts of +5.59; Dublin 49% on 132 shots with an Expt Pts of +5.54) but for those first 10 minutes they were much poorer.

Another theory for the poor start was not where teams were shooting from but who was shooting – less pressure on returns early on so midfielders/defenders were more inclined to “have a pop”. So I had a look at Mayo’s shot distribution. In the first 10 minutes 64% of their shots came from what I would state are obvious offensive players (A Moran, A O’Shea, J Doherty, A Dillon and the two O’Connor’s). From the 10th minute onwards, and adding E Regan, C O’Shea and A Freeman to this mix who didn’t have a shot in the first 10, these forwards accounted for 60% of point attempts (NOTE6).

It is difficult to attribute offensive/defensive tags to all players in today’s game but if there was a decisive split in who was shooting for teams you would expect it to show up in the team with perhaps the worst split. But it doesn’t.

Goal Attempts

To be honest I am just including the above for consistency and to help explain Dublin’s apparent ability to start faster than others. Whilst I have consistently cautioned against low sample sizes it is an overarching feature of this shot type and can explain a lot of the variance within the five minute groupings above. In total there were 137 goal attempts with just 15 in the first 10 minutes and 36 within the first 20.

Having said all that …. the Conversion Rate for goal attempts was 53% in 2016 and only crawled up to 40% after 15 minutes. With the evidence we have teams again were not converting on goal attempts early on in games.

Dublin? They had six goal attempts in the first 10 minutes scoring 3-00. 50%. And there is their apparent early start in a nutshell. They were 50% on goal attempts, 100% on deadballs (as well as Rock’s aforementioned frees he was 2 from 2 on 45s as well) and slightly below average at 39% on point attempts – giving them the aggregate of ~52% early doors.

Overview

This is based on one year’s data (NOTE7) but poor early conversion rates were definitley a “thing” that year

There is no evidence that shot selection (through Expt Pts), opposition pressure (through the simple “Y/N” flag) nor type of shooter (using Mayo as an example) is any different in the first 10 minutes to the rest of the game

It is also evident in early free taking, except for the very best in Rock & O’Connor who were on point from the very start, which somewhat nullifies the theory that it is something the opposition is doing to affect the shooting.

There are undoubtedly other factors at play. Some can be measured; first shot in the game, effect of new surroundings, debutants vs more experienced players, intensity of pressure. Some we may never be able to measure – nerves, mentality of players early on versus later in the game, etc.

But as of now, and taking all of the above into account, I cannot escape the initial gut reaction that players are just not ready – for whatever reason – early on

NOTE1 – we need to be careful with any segmentation. There are only 1,640 shots in total being reviewed here with 249 in the first 10 minutes. Segment that further by team and you get some ridiculous numbers; Kerry just have the 7 shots across two 2016 games; similarly Tipperary only have 16 shots in the same timeframe. You can’t make any judgements on those numbers. In truth I would not normally use a chart with such low volumes but I include it here as it was the chart that sparked me into looking deeper into the issue.

NOTE2 – for more on why this is so please see here

NOTE3 – I had a further look at the non Rock & O’Connor frees to see if any one player was having an effect. There was none really. 34 of the 37 were a player’s first attempt in the game which makes sense as it is uncommon for a team to have two shots at goal from a free in the opening 10 minutes.

This leads to a further corroboration that could be investigated – across the year’s how does a player’s very first free kick equate to the rest of their results?

NOTE4 – I started to grade pressure on a sliding 0 – 3 scale for the two All Ireland finals. It feels a lot more robust as having to apply a grade makes you stop and think. It will be very instructive from here on in but as of now I’m not inclined to go back over the entire season to retrospectively apply the grade(s)!

NOTE5 – This table lists all the teams with >10 shots from play in the first ten minutes. Again we are running into sample size issues.

NOTE6 – the non offensive players with a shot in the first 10 minutes were B Moran, D Vaughan, K McLoughlin, L Keegan, P Durcan & T Parsons. Other defensive players with shots post the 10th minute were C Boyle, K Higgins, S O’Shea, S Coen, B Harrison & K Keane

Note that whilst some of these could be moved into the “offensive” pot their individual shot volumes are such that it wouldn’t make a material difference to the overall point.

NOTE7 – why one year? Because for some unknown reason I didn’t track the time (outside of 1st half/2nd half) for previous years. Hell of an oversight in retrospect! The only reason I started in 2016 was I was so bored of looking at kickouts so decided to look at the rest of the game. I have one or two other pieces I newly gathered in 2016 so hoping to get another long form piece out on those

2016 Shooting review

Time for the annual review of how the season’s shooting went.

	All shots	Frees	Point attempts	Goal Attempts
2012	51.7%	70.6%	47.3%	39.8%
2013	50.5%	70.6%	44.7%	41.9%
2014	51.3%	76.8%	44.8%	47.9%
2015	53.8%	70.9%	48.5%	51.2%
2016	51.5%	71.1%	44.2%	52.9%

In truth 2016 was an average year. The three shot types listed above account for 96.4% of all shots and whilst there is some movement in each category there is nothing that really warrants further investigation.

Frees

This has been *the* most stable metric since the inception of the blog and 2016 was no different. Slight uptick but nothing exceptional. We looked at the 2014 increase here and, at the time, attributed it to better accuracy for closer in frees.

Point attempts

2015 saw an increase in accuracy for point attempts however this was a blip rather than the beginning of any trend as 2016 returns slipped back to 2013 & 2014 levels.

Goal attempts

The step up in accuracy observed in 2014 & 2015 was maintained in 2016. Teams have definitely become better at getting a return from their goal chances but not necessarily at their finishing. The above table includes any goal shot that returned a goal or a point. If we strip out the points then the goal conversion rate is 35%, 32%, 36%, 41% & 40% respectively. The step up in 2014 & 2015 is evidenced again however was maintained, rather than built upon, in 2016.