Posts Tagged ‘Deadballs’

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

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The best free taker?

June 17, 2016

This piece originally appeared in the Examiner’s Championship pullout. I had intended to link to it but it does not appear to be online so I have reproduced it below

The dearth of GAA data can lead to some curious problems. Take measuring the best free takers for example.

Normally all we get in any match report is a list of the scorers and how many of those scores were from frees, 45s etc. Rarely will their scores be put in the context of how many shots they had, how hard these shots were etc. Is the best deadball exponent (although frees make up well in excess of 85% of all deadball attempts we really should introduce penalties & 45s into the conversation) the player that scores the most? The one that converts the most? Neither?

Conversion Rates

The below table shows the Conversion Rates for any player with a minimum of 30 recorded deadball attempts over the last four Championships

Player Shots Scores Conversion Rate
D Rock (Dublin) 33 28 85%
C McManus (Monaghan) 48 39 81%
C O’Connor (Mayo) 112 90 80%
B Brogan (Dublin) 49 38 78%
C McFadden (Donegal) 54 41 76%
D McCurry (Tyrone) 39 27 69%
M Murphy (Donegal) 85 57 67%
M Newman (Meath) 39 26 67%
E O’Flaherty (Kildare) 36 24 67%
B Sheehan (Kerry) 41 25 61%
S Cluxton (Dublin) 56 31 55%

In and of itself this is noteworthy. Many would have placed the likes of McManus & O’Connor at the top of the charts but the long range experts such as Sheehan & Cluxton, who would also have had their proponents, are lower down the rankings.

This does highlight an issue with using Conversion Rates as shot difficulty (both in distance & angle to the goal) is not taken into account. Of Cluxton’s 56 attempts a remarkable 71% (29 x 45s & 11 x frees) were taken from the 45m line or further out. As a point of comparison only 10% (4 x frees & 1 x 45) of McManus’s 48 attempts were taken from the same range. How does McManus’s 81% Conversion Rate stack up against Cluxton’s 55%? Would we say that McManus is that much better of a deadball striker?

Expected Points

By dividing the pitch into segments, and using the results of well over 1,400 attempts, we are able to show what percentage of deadballs are scored per segment. We use this percentage to create an Expected Points (Exp Pts) return – along the lines of Expected Goals for soccer – for every attempt. So if a free from a specific area is converted 60% of the time the Exp Pts = 0.6pts. Doing this for every deadball attempt then allows us to compare players on a more equal footing.

Player Shots Scores Conversion Rate Conversion Rate Rank Total Pts above Expected
C O’Connor 112 90 80% 3 +7.4
C McFadden 54 41 76% 5 +6.1
M Murphy 85 57 67% 7 +4.2
C McManus 48 39 81% 2 +4.0
D Rock 33 28 85% 1 +3.7
S Cluxton 56 31 55% 11 +2.1
B Sheehan 41 25 61% 10 +0.9
M Newman 39 26 67% 8 +0.7
D McCurry 39 27 69% 6 -0.3
B Brogan 49 38 78% 4 -2.2
E O’Flaherty 36 24 67% 9 -4.1

This shuffles the ranking somewhat. The aforementioned Cluxton & Sheehan rise up the leaderboard as the difficulty of their respective attempts is filtered in. We also see the reverse as players with higher Conversion Rates drop down the ranking – Rock & Brogan noticeably.

Brogan is interesting. He only attempted three free kicks in 2015 meaning that the majority of his returns were pre the emergence of Dean Rock. Looking at the above it is easy to see why the free taking duty was passed to Rock. Although Brogan’s Conversion Rate was high at 78% – placing him 4th overall – the negative Exp Pts shows that he was missing too many easy chances. Of his 48 attempts 22 (46%) came from inside the 20m line; and he missed five of those.

Not that Rock is without his issues. In 2013 & 2014 he was an excellent 92% (11 from 12) on frees though this tally was racked up late on in games where the result was never in doubt. He started 2015 in similar form converting 93% of his first 14 however he then tailed off in the later rounds missing three of his last seven against Mayo & Kerry. Dublin have not looked to cultivate a free taker during the league campaign, so they have faith in Rock, however we won’t know if his late 2015 misses were just a small sample size or something else. If it occurs late on again when the pressure is at its most intense will Dublin be able to switch? Brogan, whilst an able deputy, is no better than average whilst Cluxton only hit one from seven in the semi-final onwards last year after coming in cold.

Cillian O’Connor

One of the more noticeable aspects of the Exp Pts table is how Cillian O’Connor & Michael Murphy rise to the top. The argument can be made that the more attempts you have the easier it is to build up an Exp Pts tally – that of course ignores that the opposite is also true. The more attempts you have the greater the opportunity to regress to the mean.

That O’Connor has maintained such standards across multiple Championships is a remarkable feat. Even more so when you consider that the methodology does not account for the majority of his attempts occurring in high pressure games (Provincial finals, All Ireland semi-finals & finals) whilst also being a load bearing totem. He has taken 76% of Mayo’s deadball attempts switching from the left to the right as well as taking high pressure penalties and the majority of Mayo’s 45s.

O’Connor’s consistency is beyond reproach and it is this, alloyed to his proven accuracy, that surely gives him the title of “the best free taker”.

NOTE: Due to space limitations I wasn’t able to expand on certain points in the article. One I wanted to address was E O’Flaherty’s returns. The above paints him as a very poor free taker which is incorrect. He is in fact a very good free taker but within his wheelhouse. By that I mean between the two 20m lines and to the left of the goals. The problem was that for years Kildare did not have a left footed free taker – or a long range one for that matter – so he was being forced to take shots that other designated free takers did not.

Frees from the Ground or the Hand; (’12 – ’15)

January 25, 2016

An article by Martin Breheny during the week had a pop at Mickey Newman for not taking a 35m free from the ground (it’s the O’Byrne cup. In January!).

Kevin Egan (@lonesharkoy on twitter and a great read in his betting columns for betswot) bemoaned the fact that the article was based on a sample size of one. One! And that maybe, just maybe, Newman might know what’s best for him when taking a free.

This is old ground. Conversion rates for frees from the hand and the ground were covered in two early pieces (see here and here). Just like now the catalyst for the original piece was commentators bemoaning the lack of a particular skill. The Breheny article, and discussion on Twitter in its aftermath, got me curious as to whether the results of the initial study had changed at all now that we had so much more data.

The short answer is it hasn’t. Overall players convert frees from the hand at a 20% rate better than from the ground.

 

Attempts Scores Success Rate
From the Hand 789 623 79%
From the Ground 405 239 59%

 

That 20% gap is a bit sensationalist. Where frees are taken from does have an impact on overall conversion rates. The majority of frees (78%) taken from outside the 45 are taken from the ground. Frees from outside the 45 are converted at a relatively paltry 38% so including these will automatically dampen the results for frees from the ground. If we exclude theme does it change anything?

 

Attempts Scores Success Rate
From the Hand 753 608 81%
From the Ground 281 193 69%

 

The answer is yes and no. Yes the difference between the two is narrowed significantly but the central tenet holds true – players are better at converting from the hand than they are from the ground. Indeed the gap has grown slightly from three years ago when similar splits saw results of 79% from the hand and 68% from the ground.

Shot attempts are further segmented by sector (see sector breakdown here) so we are able to take a more granular look.

 

Sector From Ground From Hand
4 58% 63%
5 81% 84%
6 51% 70%
7 72% 79%
8 96% 98%
9 78% 80%

 

Frees from the hand not only outperform those from the ground but do so in all sectors. Including, perhaps a touch surprisingly, the very central ones. I say surprisingly because with frees out wide I can understand how players taking them from the hands make it easier on themselves by stealing a few yards. That doesn’t really enter the equation with the more central ones.

As stated in 2013 I have no problem with people lamenting the loss of a skill but surely Mickey Newman, or any free taker, knows the best way to approach any particular free? The fact that the numbers back up using your hands is just a nice happenstance.

What’s the betting we will be revisiting this in 2017?

2015 Season Review – Part II

November 10, 2015

In Part I it was observed how the volume of shots dropped from the 2014 high of 30.9 a game back to 27.8 in 2015 (in line with previous averages from 2012 & 2013). With the quantity down was the quality affected? Yes – but in a positive manner.

The overall accuracy on all shots increased. Between 2012 and 2014 (3 years, 74 games and 4,246 shots) 51.2% of shots were converted with little year-on-year variance; 51.7% in 2012, 50.5% in 2013 and 51.3% in 2014.

2015 saw a 5.2% increase on this three year average to 53.8% (26 games & 1,446 shots). Like the deadball increase observed in 2014 (more on that below) I would be loath to read too much into one year’s worth of data however it is a noteworthy movement given (a) the size of the jump and (b) the fact that there was a jump at all after the steadiness of the previous three years.

So how was this increase achieved? Shots are broken down into three main constituent parts; deadballs account for 26% of all shots, goal attempts account for 9% with the remaining 65% coming from attempts for a point from play. The 2015 returns for all three are reviewed below.
 
Deadballs
 

Shots Scores Success Rate
2012 347 232 66.9%
2013 389 259 66.6%
2014 328 239 72.9%
2015 347 240 69.2%

One of the main findings from the 2014 review – expanded upon here – was the fact that deadball accuracy jumped after three years of remarkable consistency (although not shown in the above table the 2010 season had a Success Rate of 66.3%).

Whilst that increase was not sustained in 2015 the overall returns were still very good in a historical context. To be of an average intercounty standard your team needs to convert 70% of deadballs assuming a normal spread of distances & type.

So how is this 70% achieved?

Shots Scores Success Rate
Frees 304 215 70.7%
45s 34 19 55.9%
Penalties 7 6 85.7%
Sidelines 2 0 0%

 

Only 47 penalty & sideline attempts have been charted since 2012; much too low a number to make any concrete conclusions on. [As an aside 83% of the penalties were converted and 28% of sideline attempts]

The number of 45s converted continues on its upward curve (40% Success Rate in 2012, 50% in 2013, 52% in 2014 and now 56%) to give an overall average of 49.4% over the four years. This increase has little effect on the overall deadball Success Rates however as 45s only account for ~12% of all deadballs.

So that leaves free kicks. As ever with deadballs it is free kicks where the real movement happens. They account for ~85% of all deadball attempts (and 21% of all shots in total).

In 2014 the Success Rate for free kicks jumped to 76% from 70% & 71% the two previous years. There was no real trend as to why this was except to say that accuracy improved across the park. This year? That accuracy dropped back to 70% – bang in line with previous norms. Hello regression to the mean.
 
From play – for a point
 
Two thirds of all shots are attempts at a point for play. Though the Success Rates in the other shot types are important a team’s bread and butter can be found here.

Shots Scores Success Rate per game
2012 887 419 47.2% 17.74
2013 888 397 44.7% 17.76
2014 1012 453 44.8% 21.08
2015 963 468 48.6% 18.52

 

2015 saw a drop of ~2.5 shots per game which, though dramatic, is still ~0.75 shots higher than observed in 2012 & 2013. This lower volume did produce a higher quality however with a Success Rate of 48.6%. That is a ~8% increase on the previous two years.

Although there was a similar return in 2012 I had a look at where the shots originated to see if there was any discernible change in pattern (more shots from easier sectors). There wasn’t – if anything there were less shots from the easiest sector – Sector8 – just in front of goal.

Sector Outside 45 4 5 6 7 8 9
’12 – ’14 2% 23% 24% 17% 12& 13% 9%
2015 1% 24% 24% 18% 13% 11% 9%

 
Seeing as the ease of shot hasn’t changed the conclusion is that the quality haS increased. Ignoring shots taken from outside the 45 – which only account for ~2% of all shots – the Success Rate increased for all sectors bar Sector5 which remained stable.

Sector Outside 45 4 5 6 7 8 9
’12 – ’14 37% 37% 50% 35% 42& 71% 46%
2015 27% 43% 49% 41% 47% 75% 52%

 
 
From play – for a goal
 
The first thing to note is that the prevalence of goal attempts has not changed in any real sense. In 2015 goal attempts made up 9.4% of all shots; it was 9.6% the two previous years.

What has changed, and in truth has been a noticeable trend since 2012, is the accuracy of these goal attempts. In 2012 a score (a goal or a point) was returned from 39% of goal attempts. This has risen year on year to 52% in 2015. When we only include goals as a score (probably a more accurate measure of goal attempts!) there is still a noticeable upward trend.

Shots Scores Success Rate per game
2012 117 40 34.2% 2.34
2013 136 44 32.4% 2.72
2014 142 52 36.6% 2.96
2015 136 56 41.2% 2.62

 

Teams are getting more scores, and more goals, from their goal attempts.

So there you have it. An overall increase fuelled by better accuracy from play – both in point & goal attempts – though the increase was somewhat dampened by a drop in free kick accuracy.
 
Dublin
 
Do Dublin, given the volumes they achieved during the year, have an overbearing affect?

The answer is probably in the question – of course they do. Taking goals only Dublin scored 16 on 32 attempts in 2015 meaning that the remainder of teams converted at a 38.5% clip. In 2014 Dublin only converted 28% (9 from 32) with the remainder returning 39%. Whilst not wholly reliant on Dublin’s returns (Mayo put 6 past Sligo whilst Kerry put 7 past Kildare) the fact that they have been responsible for 24% of all goal attempts means that the year on year increase has followed their outcomes.

Similarly when going for a point Dublin converted 57.3% on 17% of all attempts recorded with all other teams converting 46.8%. It is not just Dublin here however as Mayo converted 56.7%.

We use averages as a starting point as, with a large enough sample size, these “outliers” will be subsumed by the whole. However when viewing the Grade A teams (Dublin, Mayo, Kerry) a premium needs to be added to the average when reviewing their play whilst Grade B teams – those trying to break through (Cork, Tyrone, Galway) – need to aim far higher than the average.

An Ode to Cillian O’Connor’s right foot

October 28, 2014

Originally this was going to be a follow up to the deadball accuracy piece in which I would highlight any interesting tidbits that had popped up whilst reviewing the past three years. Instead it is going to be an ode to Cillian O’Connor.

Attached below is the chart I initially posted on twitter (@dontfoul) showing the accuracy for the players with the ten most attempts since 2012.

 

Player deadball

 

O’Connor came out on top in terms of Success Rate. But he did so whilst also attempting the most deadballs. He had volume on top of his accuracy. He also maintained this accuracy in some of the most highly pressurised situations. In the three year span that the returns covered Mayo have appeared in 13 TV games with three quarter finals, four semi finals and two finals amongst them. Most impressive.

 

Deadball accuracy v2

 

What is even more impressive is the fact that O’Connor has recorded the highest weighting in this interval as well. He is not just tapping over simple 14m frees to maintain his Success Rate. His weighting reflects the fact that he is converting frees of an above average difficulty at an above average frequency (as a counter point note Bernard Brogan – a well above average Success Rate but a barely positive weighting – he is merely converting the frees that an average free taker would convert but because of Dublin’s volume of attacks he gets to take more frees).

It is not just volume that is leading to a higher weighting either. On current trajectories only Cluxton is in line with his weighting. So how has he done it?

 

Deadball Type Attempts Scores Success Rate Average Weighting
Frees
inside 20m 24 23 96% 88% +2.64
20m to 45m 35 30 86% 71% +5.375
outside 45m 3 0 0% 40% -1.210
total 62 53 85% 72% +6.803
45s 7 4 57% 47% +0.630
Penalties 3 3 100% 82% +1.778

 

O’Connor’s accuracy is above the average in all three of the deadball categories – frees, penalties & 45s – that he has attempted. His weighting has been aided by converting three penalties (for a longer explanation on why penalties have such high weighting see here) but on the flip side he has attempted three frees from outside the 45 for which he patently does not have the “legs”. As an illustration see a chart of all his deadballs, excluding penalties, this year.

 

OConnor14
x = missed, disc = score,black = free, white = 45

 

O’Connor’s only missed two frees from inside 40 metres in 2014 and one of those was the desperation goal attempt in the semi final replay at the end of extra time. His accuracy from c40 metres in is genuinely exceptional.