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.

Comparison of League & Championship returns

Comparing league and Championship

I have only ever used Championship games when creating specific averages or metrics. In the main this was due to laziness on my part as I wasn’t really tracking League games but there was also a part of me that viewed League & Championship as sufficiently different to be treated individually. Separate entities. Different games on different pitches in different weather.

But then I got notions and tracked the 2018 league. Was the league data sufficiently similar to that of recent Championships allowing us to augment existing datasets and produce more robust outcomes? Or did the “gut feeling” that the two should be kept separate stand up to even the most rudimentary of reviews?

In the end 22 League, and 32 Championship, games were tagged (a fancy way of saying “noted a lot of different things within a game”) throughout the year. A nice, healthy, robust possession count of just under 5,000.

Given the size of the dataset, and the fact that the games reviewed were within the same calendar year, any differences should really be due to the competition, and its peculiarities, rather than any observed changes in styles throughout the years. (And as a nice aside we’ll also have a clean, comparable, dataset to test the effects, if there are any, of the new rules being introduced in 2019).

Game flow

At the outset it became obvious, despite the best intentions of curtailing the review to one season, that we were not comparing apples with apples. Within the 32 Championship games there were quite a few where the disparity between the two teams’ quality was very evident (somewhat expanded upon here). So the 2018 Championship returns were subdivided further into (a) all Championship games and (b) those games between Division1 teams only (Dublin, Tyrone, Galway, Monaghan, Donegal, Kerry, Kildare & Mayo) of which we had a healthy number – 15 in total – thanks to the introduction of the Super8s.

And we have our first surprise. There were as many possessions per game in the league as there were in the Championship; and the differences between the two Championship subdivisions were small enough to be deemed immaterial. I say “surprise” as, based on nothing more than intuition, I fully expected there to be more possessions during the League as a result of increased turnovers through weather, heavy pitches and teams not being quite at their peak.

Now there were more turnovers during the league – five a game – but this was counter balanced by how teams used the ball. There is an appreciable step up in attacking production across the Championship with higher Attack and Shot Rates. Both these then lead to noticeably more shots (~8.5%) per game. More shots equal more kickouts.

Those turnover possessions “lost” from the League to the Championship were regained through extra kickouts resulting in the immaterial movement in the total number of possessions.

Kickouts

Viewing kickouts in isolation that point is further evidenced; kickouts account for 49% of all possessions in the League but 54% in the Championship.

Teams were much more on point on kickouts throughout the Championship retaining possession at a much higher rate than they did during the League (77% v 70%).Teams won more short ones (95% v 91% during the league) as well as more of the longer ones (61% v 56% during the league).

One of the contributing factors here could be the well-worn training cycle within GAA where teams work on kickout routines coming into the Championship as the winter months are used to lay down a fitness block.

Interestingly this is the first time we see a break in how the two Championship subdivisions perform. During the league the kickout team won 56% of their own kickouts that went past the 45; this stepped up to 61% in the Championship and again to 66% when we segment just the Div1 teams.

A jump in retention rates from 56% to 65% on kickouts past the 45 is quite noticeable – especially on those kickouts that should be the most contestable. The teams, and the quality of the opponent, haven’t changed. Instead teams have just improved.

This stepped increase in retention rates, both from league to Championship and within the Championship subdivisions, lends credence to the above supposition that teams “work on” their kickouts more in the lead up to Championship than they do in the League with the better teams, i.e. Div1, being more successful at implementing their plans.

Shooting

As noted above there are more shots attempted during the Championship. But that alone does not account for the higher scoring rates observed during the summer. Teams are more efficient with that extra shooting. And not just in one particular phase but across all three – point attempts, both from play and deadballs, as well as goal attempts.

Point attempt Conversion Rates (from play) during the League were lower than those in the Championship. But not massively so. They are probably within any margin of error so further work is required to confirm if this a League v Championship, early season v late season, bad weather and heavy pitches v good weather and “proper” pitches thing or just one season noise.

Deadball returns have improved the last two Championships (2018 – 74.0%, 2017 – 73.5%, 2016 – 69.0%, 2015 – 68.9%) so the 72.2% achieved within the 2018 League feels like a lag. Again though it is within any margin of error so it is probably wise to err on the side of caution and say that the differences are inconclusive.

Goal shots are probably the most eye catching numbers with just 32% of shots converted during the League and 39% during the Championship. Again those Championship games involving the Div1 teams saw another slight bump to 41%. The volume of goal attempts in those Championship games was less at 4.2 per game (as against 4.7 during the league) yet they were converted at a much higher rate. Why? More work done on finishing moves? Random one year fluctuations on small sample sizes? Effect of must win games in the Championship ensuring players take points off less clear cut chances? That most unsatisfactory of answers … to be determined.

Instinctively the Conversion Rates for all three elements, deadballs, point attempts and goal attempts, being lower in the League feels right. As stated I have never been a fan of mixing data from the two competitions and whilst the disparities are probably small enough for some the differences just reinforce my original belief that, without further analysis, we shouldn’t just lump the two competitions together to get bigger volumes.

So there you have it. At a macro level League games are similar to Championship games with the same number of possessions and comparable Conversion Rates. But get under the hood and the makeup of each component differs just enough to warrant (for me anyway) keeping all metrics for the two separate.

One final quick note

Defence

Unfortunately there are no great defensive metrics per se. The sign of a good defensive performance is usually evidenced by the absence of good offensive metrics for the opposition. But that doesn’t really work here when we are looking at averages in the round as everything just becomes an aggregate blob with no real decipherable differences.

Some specific defensive metrics we can look at are turnover rates (though the assumption that all turnovers are induced by the defence doesn’t hold much water), how often teams get in for an attempt at goal and the pressure faced by teams when shooting.

Focussing on the Div1 teams we can see a tightening up on the defensive front with a ~10% increase, from the league to the Championship, in the number of shots taken under strong pressure. There is also a ~10% increase in the number of possessions it takes to get a shot on goal.

As well as getting more clinical on their use of possessions and kickouts the Div1 teams also tightened up defensively (balanced by more efficient shooting). Everything trends towards the Championship just being that step ahead of the League. Sometimes what you see really is what you get.

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.

Effect of game state and opposition on Expt Pts

Despite creating an Expt Pts model (outputs are here for anyone interested) I am acutely aware of its limitations. There are so many factors that can go into whether a shot is successful or not that only taking three elements (shot type, shot location and whether it is a goal attempt or not) seems arbitrary. It leaves the metric incomplete.

Now don’t get me wrong. I still believe that Expt Pts is a better metric than Conversion Rates (which are better again that just counting wides!) but at the moment it is a tool that allows you to quickly hone in on particular areas of the game rather than being an absolute barometer.

So the question arises. How do we make it better?

The first port of call is to look at what other sports are doing. Both Soccer (through Xg) and the NFL (through DVOA) place emphasis on the game state and the quality of opposition.

We are limited in the GAA by the availability of raw data but at Christmas I was able to post the below graph on Twitter (@dontfoul) which shows that there is definitely a game state effect. (People were generous enough not to point out my spelling of “deficit” – D’oh!)

Was there also a “strength of opposition” effect as well? And if so can we combine the two to create a more refined Expt Pts?

Strength of Opposition
The games used in this review are 2015 – 2017 Championship games. In that timeframe we want to somehow grade/tier all the teams to see if playing up, or down, to your level has an effect on Conversion Rates.

So how do you Grade a team (see Note1)? My approach was a mixture of subjective and objective. From ’15 to ‘17 three teams – Dublin, Kerry and Mayo – were ahead of the rest occupying 9 of the 12 semi-final berths. Go back to 2014 and it is 12 of the 16 semi-final berths with the only non-Big three to make the final in those four years being Donegal. They are our (subjective) Tier1.

It is possible to make a whole army of mini tiers all the way down the 32 counties thereafter but we will then run into sample size issues for any output. Plus it is very rare for those teams in Division4 to compete in TV games, which is essentially what we have to work with, so we do not need a whole host of Tiers. Instead we just need big enough ones that are appropriate.

So I came up with a rules based approach to split the remaining 28 counties (excluding New York & Kilkenny) more or less in two.

All teams in that years’ Division1, outside the Big 3, plus all teams in Division2 are Tier2 as is any team from Division3 and 4 that made the Championship QF. This gives us an objective grading system with the ability to upgrade teams that played well but also stable enough year on year.

It is not perfect. It has an element of subjectivity. But it does the job required.

So we have our teams graded according to their overall relative strength. Does it work? Is there an effect? Intuitively it should. It just makes sense that Dublin will convert more against Antrim than they would against Tyrone. Or that conversely Laois would struggle when playing up a level against Dublin but less so when playing Wexford.

Also we have seen this effect in action previously when we calculated Conversion Rates after overlaying the pressure applied to shots (implication being that “better” teams will apply more pressure to shots and vice versa).

Finally we also have an inbuilt test. If the Grade shows itself up in open play it really shouldn’t from frees. Why would taking a free against Tyrone be any different than trying the exact same free against Offaly? So the expectation is that when we break teams’ Conversion Rates down by Grade attempts from play will vary but those from frees won’t.

And that is exactly what happens.

Irrespective of the opponent Conversion Rates on frees (only frees taken inside the 45 were included to remove as many outliers as possible) are very stable. The volumes are decent as well with just over 1,000 frees included.

The big eye opener however is what happens to attempts from play. As you go up the Grades/Tiers there is steady fall off in Conversion Rates. If, for example, Meath play Dublin then the Conversion Rate is 38.7%. Against Kildare it increases to 45.6% and against Wicklow it jumps again to 51.2%. The same phenomenon can be seen in goal attempts. The “better” the opposition the lower the Conversion Rates.

This all makes sense but the implications for Expt Pts, and anyone looking to use any version thereof, are quite big. Expt Pts currently run off averages … those averages are taken as a whole from all games. They need to be calibrated for opponent.

Game State
So now that we have strength of opposition what about game state? The original chart above showed that there was an affect but that was too simplistic. Registering all scoreboard differences as the same is not right (taking a shot two points down in the first half is not quite the same as having an attempt two points down in the 60th minute). We have to further refine the criteria.

Right now we don’t need to define what the optimum criterion is. We are just trying to show/prove if there is indeed an affect. To that end I have extracted all shots taken under a “clutch” situation – defined as any shot in the 2nd half of a game where there is only three points in it – and compared them to “non-clutch” shots.

Again we could define it differently – say only include games within two points from 60th minute onward – but this will give us volume issues.

So what are we expecting to see? Conversion Rates to be lower across the board in clutch situations. The real test however is that that frees should, unlike when looking at Grading above, be affected by this new scenario. If there is “scoreboard” pressure it should affect frees as well as point attempts.

And once again there is an affect. The Conversion Rates for both frees and point attempts from play disimprove in tighter game scenarios. Again all very sensible.
Finally overlaying the two we can see that the greatest discrepancies happen in, subjectively, the scenarios people are most unaccustomed to.

Lower level teams, with a shot of winning the game in the 2nd half against higher quality opponents, convert only ~31% of their point attempts as against ~40% for the rest of the game. The “deer in the headlights” syndrome.

There is also a minor drop off when teams are playing against opponents of similar, or lower, ability but it is nowhere near as stark.

So there we have it Scoreboard pressure is indeed a thing. The opponent grade matters. And whilst very useful the simplistic Expt Pts model is nowhere near complete.

Appendix
Note1; We could look at grading teams via their defensive performance in that period but some teams rarely, if ever, compete in TV games. How do you grade a defence based on performance metrics if you’ve never seen them?

Note2;

2017 Grade2 teams
Division1 that year; Tyrone, Donegal, Monaghan, Cavan, Roscommon
Division2 that year; Cork, Derry, Kildare, Meath, Down, Galway, Fermanagh, Clare
Division 3 or 4 that made it to the QF; Armagh

Grade 3 – Westmeath, Laois, Louth, Wexford, Limerick, Longford, Sligo, Offaly, Tipperary, Wicklow, Leitrim, Waterford, Antrim, London, Carlow, London

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

Moving from Weighting to Expected Points

Expected Points

Since the blog’s inception weighting has been the main comparative tool. Towards the back end of the 2015 season Expected Points (ExpPts) began to be used in the commentary. This is a very similar concept to Weighting, in that it is created using historical averages, but (hopefully) it is much more instinctively understood and one that the blog will be switching to in 2016.

On top of this there will be a major tweak to how the old weighting, and now ExpPt, will be calculated. Up until now the weightings have been generated purely on a “percentage of shorts scored” basis with the conditions being

• Where did the shot originate
• Was it a deadball (& what type) attempt or from play
• Was it an attempt on goal

One omission from the above, amongst many! is the impact the defense was having on shooting returns. This changed with the Big Fish Little Fish piece where we observed big shifts in Conversion Rates depending on the opposition. This concept will now be introduced to ExpPts.

Converting Weighting to ExpPts

This switch is relatively straightforward for everything bar goal attempts. In the 2015 Review we saw that 37% of point attempts were converted from Sector 4. In old parlance if you converted a shot from here your weighting was +0.63 and if you missed it was -0.37. The weighting was based on the Conversion Rate.

The same principle applies for ExpPts. As 37% of all shots are converted a player is expected to score 0.37 points for every shot taken from here. If he converts that’s +0.63 ExpPts (1 – 0.37) or -0.37 if he misses. Do that across all the shot types in all the sectors and we have an ExpPts model.

Goal attempts are slightly trickier. The weighting only ever had a value between 1 and -1 however on goal attempts we know there are three different returns; 3 points, 1 point or 0 points. There can be no direct correlation between goal shots and weighting. Instead we’ll create an ExpPts for goal shots.

Over the past four years 531 goal attempts have been charted with a total of 192-50 scored. This haul equates to 1.18 points per attempt ((192*3)+50)/531). So the ExpPts for a goal attempt from play, without overlaying any factors, is 1.18.

As ever this is a linear application. Not all goal shots are created equally (a pass across the square for an easy fisted goal is very different to a desperation shot with three defenders and a goalie in the way) but whilst shot location has been charted goalie & defender positions have not. Further sub division of goal attempts will be the main 2016 project.

Deadballs are easier. All bar 38 (29 penalties & 9 attempts at goal from a free) of the 1,411 charted were point attempts so we have a direct correlation between weighting and ExpPts using the above methodology. For the record the ExpPts for a penalty is 2.48 (24 of 29 converted) and for an attempt at goal from a free is 0.33 (1 goal scored from 9 attempts).

A nice example of how this works is the drawn Mayo-Dublin semi-final. Below is the ExpPts of both teams throughout the game. You can see how Mayo tracked their ExpPts throughout (i.e. were basically getting what you expect from the shots attempted) but Dublin were nearly always ahead with the gap widening around the 55th minute. Here they scored 2 goals getting 6 points versus an expected return of 2.36.

Introducing Defensive Adjustments

Using the above model gets us closer to reality on how a player or team performed shooting wise however as noted it is still very “simple”. There are many factors that are not taken into consideration one of which is the quality of opposition. Scoring a point against Mayo surely cannot equate to scoring a point against xxxxxxx (insert name of a particularly poor team)

During the Big Fish Little Fish article it was highlighted that there were large discrepancies in returns depending on the opposition. During that article teams were split into semi-finalists and non semi-finalists and the outcome of shots taken in each type of game compared. This gives us four distinct “game types”

• semi-finalist shooting against other semi-finalists
• non semi-finalists shooting against non semi-finalists
• semi-finalists shooting against non semi-finalists
• non semi-finalists shooting against semi-finalists

In the Big Fish Little Fish piece the argument was made that the non semi-finalists probably need to be further sub divided (into Div1 & Div2 v Div3 & Div4 perhaps) but we do run into sample size issues. We could also arbitrarily pick teams to be “Big Fish” (Dublin, Kerry, Mayo, Donegal definitely. Tyrone?). Again one to be reviewed with more data.

So to wrap up we now have two Expected Points models; a bog standard one (ExptPts) based on pure averages – this is what was introduced within the two league semi-final reviews. The only difference with this metric from historic weightings is goal attempts with every goal shot having an ExpPts value of 1.18.
We have also created a defensively adjusted Expected Points (will have to come up with some shorthand. I’m not writing that out every time!). Where this will really come to light is in the early round of the Championship when the player’s returns will be adjusted based on the quality of opposition.

Big Fish Little Fish

2015 saw a number of very one sided games between the big 3 (Dublin, Kerry & Mayo) and the rest. Kerry’s shellacking of Kildare in the quarter finals. Dublin ripping through Leinster. Mayo destroying Sligo.

The blog, whether using weighting or expected points, has always used averages as a base. The more positive the weighting the better the team played when compared to the average. But these one sided beat downs must be affecting the averages. And if they are to what extent?

On top of just pure skill differentials there must also be something else happening in these games. How is it that a team like Kildare can beat Cork one week but lose heavily to Kerry the next? Do weaker counties play differently when facing the bigger teams? How do the big teams’ numbers compare when facing each other as opposed to the cannon fodder earlier in the Championship?

What is a Big Fish?

First we must define a big team. It would be very easy to just pick Dublin, Kerry & Mayo but we run into sample size issues when isolating games between similar teams. The semi-final replays of the last two years have helped but restricting “big teams” to the aforementioned three would leave us with only eight games over four years. To expand the big team pool all semi-finalists in any particular year are included (See Note 1 in the Appendix). This increases the volume of games between big teams to 14.
 
Deadballs

	Shots	Scores	Success %	Shots per game
non semi-finalist v non semi-finalist	465	323	69.5%	6.84
non semi-finalist v semi-finalist
non semi-finalist	358	241	67.3&	6.75
semi-finalist	365	257	70.4%	6.89
semi-finalist v semi-finalist	184	124	67.4%	6.57

 

The most remarkable aspect of this table is its blandness. No matter the game type the difference in volumes of deadballs per game, and the accuracy of those deadballs, is negligible.

In a way this makes sense as there is no real outside influence when you take a free (frees make up ~85% of all deadballs so in many ways the term deadball & free are interchangeable). It is just the player and the ball; the opposition cannot influence the outcome (See Note2 in the Appendix)

What is surprising is that in games between semi-finalists & non semi-finalists the volume of shots is the same. Yet we know that the volume of attacking play is heavily weighted in favour of the semi-finalists. In 12 games of this nature (semi-finalist v non semi-finalists) this year the semi-finalists averaged 41 attacks whilst the non semi-finalists averaged 32. The volume of deadballs has stayed steady over the years meaning that the semi-finalists gave up a deadball shot every ~4.7 attacks; the non semi-finalists were at ~6.1.

Now maybe this is due to better attacking play from the semi-finalists, enabling them to avoid getting caught in possession, but equally it could be the better teams fouling early to get back into position and/or stop a goal threat. Or maybe it’s just sympathetic referees in blow outs! But whatever the reason the better teams do foul at a higher rate when faced with inferior opposition.

For point from play
 

	Shots	Scores	Success %	Shots per game
non semi-finalist v non semi-finalist	1,214	544	44.8%	17.85
non semi-finalist v semi-finalist
non semi-finalist	847	338	39.9%	15.98
semi-finalist	1,156	598	51.7%	21.81
semi-finalist v semi-finalist	533	257	48.2%	19.04

 
The deadball overview may have been unremarkable – this is another beast entirely.

Non Semi-Finalists

When playing each other non semi-finalists converted point attempts at 45%. This dropped to 40% when playing semi-finalists. The volume of shots also dropped by ~10%.

The deadball returns showed that all things being equal there are no real differences when comparing the various game types. Things are not equal here however as the opposition has a huge bearing on your returns.

But how does that manifest itself in terms of the reduced accuracy noted above? There are two main elements that have been tracked to date (there are others!) where the opposition can have an impact on your shooting – where the shot is taken from and whether the shot is taken under pressure.
 
Non semi-finalists’ % of all shots taken

	Outside 45	4	5	6	7	8	9
v non semi-finalists	2%	24%	25%	16%	13%	11%	9%
v semi-finalist	3%	26%	23%	16%	14%	11%	8%

 
Nothing to see here. Irrespective of opponent non semi-finalists’ shooting tendencies remain constant. Remarkably so.
 
All teams – frequency & impact of pressure

	Pressure applied	Success %	Pressure applied	Success %
non semi-finalist v non semi-finalist	53%	42%	47%	48%
non semi-finalist v semi-finalist
non semi-finalist	57%	38%	43%	42%
semi-finalist	47%	48%	53%	55%
semi-finalist v semi-finalist	48%	43%	52%	53%

 
If the lower conversion rates are not due to shot location are they impacted by pressure? As expected semi-finalists do manage to pressure more shots than non semi-finalist in games between the two – 57% v 47%.

That however does not tell the whole story. “Pressure” is recorded as a binary “yes/no”. The *type* of pressure – be that multiple players, or tighter marking from better defenders – is not accounted for. That, on top of the mere presence of extra pressure, could easily be responsible for the drop in the overall Conversion Rate from 45% to 40%.

What’s really interesting though is what happens when there is no pressure applied. You would think, both from a logical standpoint as well as from the results observed in deadball situations, that the returns here would be similar irrespective of the opponent. There is no outside influence. But in contests against other non semi-finalists, where no pressure was applied, players had a return of 48%. In the same scenario against semi-finalists the return dropped to 42%. That’s a bigger drop off than when pressure is applied (see Note 3 in the Appendix)!

Semi-finalists

The fact that something “strange” is happening with non semi-finalists is further confirmed when we look at the semi-finalists. As expected there is little difference in the returns when no pressure is applied; against non semi-finalists they convert 55% and against other semi-finalists they convert 53%. The difference is well within any margin of error.

The earlier point re the type of pressure being different is also underlined. Semi-finalists, when facing non semi-finalists convert 48% of shots taken under pressure. Against other semi-finalists that becomes 42%. The assumption again being that the drop off is due to the *type* of pressure being applied by the better teams.
 
Goal Attempts

	Shots	Scores	Success %	Shots per team	pts per attempt
non semi-finalist v non semi-finalist	141	55-16	39.0%	2.14	1.28
non semi-finalist v semi-finalist
non semi-finalist	118	31-12	26.3%	2.23	0.89
semi-finalist	219	96-14	43.8%	4.13	1.38
semi-finalist v semi-finalist	82	34-08	41.5%	2.93	1.34

 
The same patterns observed when attempting a point are observed when going for a goal.

For non semi-finalists the accuracy plummets as the opposition quality increases. For semi-finalists the accuracy is more or less maintained but they manage ~50% more goal attempts per game when facing non semi-finalists.
 
 
Based purely on point taking ability and volumes (we’ll take deadballs as a wash) the big fish of this piece have about a five point head start. That grows out to ~8.5 points once we bake in goal attempts. That is a huge gap to plug but the little fish of this piece are not helping themselves. Too many of their own shots are taken under pressure whilst they are also allowing their goal to come under siege. That’s not to mention malfunctioning when under no pressure. Nor the unedifying thought that they are not cynical enough and should be following the big fish’s lead and fouling more.
 
 
Appendix
 
 
Note 1: The big fish are thus

2012 games; Cork, Donegal, Dublin & Mayo
2013 games; Dublin, Kerry, Mayo & Tyrone
2014 games; Donegal, Dublin, Kerry & Mayo
2015 games; Dublin, Kerry, Mayo & Tyrone

There is an argument to be made that the non semi-finalists – the “little fish” of the piece – needed to be further subdivided whether that be by league position, losing quarter finalists or some subjective manner. Lumping the Cork & Monaghans of this world in with the Leitrim & Longfords is not fully representative. This is accepted but again we run into sample size issues.

Still to be honest I nearly did it anyway so that I could use the “big fish”, “little fish” and “cardboard box” nomenclatures. Next time.

Note 2: Not strictly true I guess as the opposition can – in many cases – choose where to foul and thus impact the outcome of those frees. Where deadballs attempts originated from in games between semi-finalists & non semi-finalists are listed below.
 

	Outside 45	4	5	6	7	8	9
v non semi-finalists	13%	18%	20%	18%	5%	19%	7%
v semi-finalist	10%	19%	25%	17%	6%	18%	5%

 

The semi-finalists do get more shots off from Sector5 whilst giving up more outside the 45. This could be used to support the “clever” fouling argument in that they are fouling earlier to protect the defence. It could also be the case that the weaker teams take whatever opportunity they get so have more long range attempts.

This is the only real difference however. Generally speaking teams foul in the same areas.

Note 3: The sample size here is robust (~950 shots) so the issue is real. Pressure is a nebulous thing – one man’s pressure is another’s lazy arm – so consistency in the definition could definitely be an issue. There is however only one source tracking the games so we have to assume there is consistency.

Other issues like game state (more pressure on shots in closer games), game type (semi-finalists by nature are meeting in bigger, more pressurised games) or pitch (shooting in Croke Park into the hill against the Dubs vs Clones perhaps) will all have an undetermined effect.