Archive for the ‘Overview’ Category

2018 Division 1 Review

April 27, 2018

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.

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Expected Points (Expt Pts); numbers to use

January 25, 2018

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

January 27, 2017

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?

November 10, 2016

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.

graph-1-overview

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

graph-2-by-team

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

graph-3-expt-pts

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

graph-4-by-free

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

graph-5-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

graph-6-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

November 2, 2016

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.