Posts Tagged ‘Review’

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.

Big Fish Little Fish

November 30, 2015

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.

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.

2015 Season Review – Part I

October 29, 2015

Reviewing the major statistics – Attacks, Attack Rates, Shots, Shot Rates & shooting accuracy – from the season just gone, and comparing them to previous seasons, has become a staple of the blog. We do it for two main reason; the first to see if there are any major trends, or indeed areas of complete randomness, jumping out whilst the second opens a window on any elements we should be taking a closer look at.

We tracked where attacks originated for the first time in 2014. This was extended in 2015 to cover all possessions. This enables us to have a complete picture of an average (see NOTE 1 below) Championship game. How often teams have the ball, where those possessions emanate from and what they do with them.

Given this expansion I am going to split the review into two parts; below concentrates on everything up to shooting whilst the second piece (due soon!) will focus on shooting trends and accuracy. So without further ado here are the overall returns up to the point of shooting

 

Year Possessions Attacks Attack Rate Shots Shot Rate
2012 35.3 27.0 76.6
2013 36.3 28.3 77.8%
2014 39.8 30.9 77.5%
2015 49.7 36.8 74.0% 27.8 75.7%
avg 37.0 28.5 76.9%

 

Possessions volumes & origination

How a possession is defined is expanded upon within the definitions page but essentially a possession starts with gaining “control” (see Note2 below) of the ball and ends with either a shot or the opposition gaining control of the ball (a turnover). For the sake of this piece all turnovers are equal – whether forced or otherwise – with the only defining characteristic being where on the pitch control of the ball was obtained.

On average a team has the ball ~50 times (49.69 to be exact!) a game. This has always seemed low to me – essentially 2 possessions every 3 minutes allowing for injury time. I guess it emphasises the need to squeeze the most out of each possession though as ever we must be wary of averages. Especially averages built on one year’s data.

Where this single piece of information may be of most use is when chasing (or defending) a lead with “x” minutes to go. You can extrapolate how many possessions you’ll have; how many shots; what you need to break your way; go for points or when to push the “go for goals” button. Game endings can take on a life of their own but knowing you’ll have 10 possession in the last 15 minutes gives you a good starting point.

Where the possessions emanated from are listed below

 

Possession Origination # % all possessions
Own kickout 16.0 32%
Opp Kickout 6.9 14%
T/over own 3rd 17.2 35%
T/over mid 3rd 6.1 12%
T/over opp 3rd 0.9 2%
Other 2.6 5%

Essentially one third of all possessions come from a team’s own kickout, one third comes from ball gained inside a team’s own 45 with the remaining third spread across the game. There is huge emphasis placed on kickouts – both your own and the opposition’s – but teams get as much ball up to the opposition’s 65 from broken play as they do from kickouts. Do teams place as much emphasis in moving the ball, or defending counter attacks, as they do on kickouts? Does the general commentary? I’m as guilty as anyone. Tracking kickouts is easy but how (and why?) teams are able to move the ball in broken play is every bit as important.
 

Attack & Shot Rates

Last year we saw a spike in attacks; rising 11% from 35.80 in the two previous years to 39.83 in 2014. At the time quite a bit of the increase was being attributed to the black card (well the numbers weren’t used in the wider commentary – it was more the black card being acclaimed for leading to open play) however here on the blog judgement was reserved. The black card was part of the reason but it could as easily have been a one year outlier as much as anything.

That reticence seems well founded now as the 2015 average attack numbers have dropped back to 2013 levels. Looking across the four years a team will create an average of 37.0 attacks and, using 2015 data only, 74% of all possessions end up as an attack.

From 2012 to 2014 the average number of shots per game rose from 27.0 to 30.9 – a 14% increase. Over that time there had been a rise in shot rates but nowhere near the 14%. Instead the increase in shots had risen in line with the volume of attacks (with a small bounce from increased efficiency as the shot rate went from 76.6% to 77.5%).

In 2015 the number of shots per game dropped ~10%. As we can see from the above this is to be expected when the number of attacks drops but on top of the drop off in attacks there was also a drop off in the shot rate. For the first time in four years the Shot Rate came in below 76%. Again this is one of those movements that is probably more of a blip, or general randomness, but it is still worth monitoring. The sample size – 1,446 shots in 2015 & 4,246 in the previous three years – is robust.

So there we have it. On average a team has 50 possessions a gam converting 74% of those possessions to an attack and 77% of those attacks to a shot leaving a total of 28.5 shots a game. What teams do with those shots will be expanded up in Pasrt2 of the review.

Note1: Averages hide a lot and the fact that Dublin have been the pre-eminent attacking team in the period that these returns cover (probably involved in ~20% – 25% of all games) will have an effect. There is definitely scope for rerunning these numbers with/without Dublin and also in games completely excluding Dublin (or even Croke Park for that matter).

Note2; Control, and where it starts and ends, can be a very subjective thing. I tend to veer away from subjectivity – though to start to expand and add value I am going to have to dip my toe in that water (more of that anon) – however here I would estimate that ~98% of the time who is in control of a ball is obvious. The other 2%? I guess we’ll just have to go with it ….