Archive for the ‘Review’ Category

Effect of game state and opposition on Expt Pts

July 5, 2018

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

And that is exactly what happens.

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

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

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

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

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

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

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

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

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

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

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

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


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

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


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.

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


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.


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.


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.


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

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

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

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

Expected Points (Expt Pts); numbers to use

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)

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.

2017 Expt Wins

January 22, 2018

So in what is now becoming an annual exercise let’s review the 2017 season through the prism of Expected Wins (Expt Wins).The 2015 and 2016 versions of this article can be found here and here

For the uninitiated Expt Wins uses bookmaker’s odds (note 1), as a sort of independent arbiter, to see which teams over (or under!) performed versus what was expected on a game by game basis. It is a much better fairer view than sheer win percentages given (a) how relatively short the season is for most teams and (b) how uneven the Championship can be in terms of the quality of teams facing off against each other.

Table 1; 2017’s best and brightest

Unsurprisingly the top10 is peppered with teams that were promoted. This makes sense as for the majority of teams the league makes up at least two thirds of their season.

Carlow had an excellent season – but it was no fluke; they were also top5 in 2016. Over the past two seasons they have won 11 games when they were only expected to win ~7.7. And they managed to top the 2017 table despite losing as 1/16 home favourites against London. 1/16, without accounting for the bookmaker’s margin (see note1), implies a 94% win probability. Win that game and their “above Expt Wins” total would be twice that of second placed Louth. That loss is the shortest price loss in the database and must be one they desperately want back. There is no guarantee that Carlow would have gained promotion had they beaten London – as Wexford no doubt would have put greater emphasis on their final two games – but they must be absolutely kicking themselves every time they think of that game. And yet – they still topped the 2017 table despite this loss.

Now I am by no means an expert on the ins and outs of Louth football – and you have to think they have a good ‘un in Pete McGrath – but you have to feel for Colin Kelly. Back to back promotions. A 59% win rate over that period which lands them 5th on that metric behind the likes of Dublin, Tyrone, Kerry and Kildare (themselves aided by back to back promotions) and along with Carlow the only team to finish in the top5 Expt wins both years.

The two Championship campaigns were poor in comparison (played 6 won 3 with three loses of 4, 6 and 9 when stepping up against Derry and Meath) but still … be careful what you wish for.

In 2016 Tyrone and Cavan were in the top10 following successful promotions from Division2 and the trend holds true for Galway and Kildare. Division2 is always very tight – just under half (27 of 56) of all games in the last two years had a zero or one point handicap. Extend that to two points and 80% of the games are covered. Win enough games to gain promotion in these tight contests and you are well on your way to outperforming expectations for the season.

Table 2; 2017’s laggards

This is a mixed bunch of
1. Division4 teams who struggled to register wins and who are perennially down the bottom of these rankings – Limerick, Waterford, Wicklow
2. Teams that had a disastrous season – Cavan, Laois, Derry
3. Very good teams that didn’t get the job done enough – Kerry & Mayo
4. Cork!

Taking the four cohorts in order

1. The worst team in 2017 was (subjectively) Wicklow but no matter how bad you are when you play your peers in the league you are always given “some” chance. Wicklow’s seven league appearances saw them chalked up at odds of 8/11, 3/1, 5/6, 10/3, 11/2, 6/5 and 6/1. When we remove the bookmaker’s margin that equates to an expectation of two wins. And that’s for the “worst team”. Limerick’s odds were 8/13, 11/10, 13/8, 1/10, 8/15, 11/8 and 1/10 which comes out at just over four wins.

No matter how poorly you are viewed under Expt Wins you will always be expected to notch up at least two wins and maybe four or five … if you struggle to win games full stop you will always be down the bottom end of this table.

2. All three of Cavan, Laois and Derry were relegated and whilst combined they won 4 of 10 Championship games three of those victories came against Division4 teams when they were heavily favoured. At a very high level this cohort win the games they are expected to win, lost the ones they were expected to lose and came out the wrong side of way too many 50:50 calls

3. Mayo being so low on the table is easy enough to explain; in the three games that they drew Mayo were 1/5, 1/6 and 23/10 – those three games alone account for their negative Expt Wins. Kerry are slightly different. They may have finally managed to beat Dublin in the Division1 final but outside of that they failed to win half their games – with three of those games coming against Mayo when a good favourite (2/5, 1/2 and 8/13). They were almost prohibitively favoured at 1/20, 1/5 & 1/6 in the three Championship games that they won. That mixture (winning when big favourite, losing/drawing when favouritism is less obvious) is a recipe for a poor Expt Wins season

4. Cork. Ah Cork. For the second year in a row they appear in the bottom5 but can you imagine how poor they would look had Waterford managed to tack on one more point when Cork were 1/50? Cork were middle of the pack on win ratio (winning 41% of their games) but were overturned by Tipperary as a 7 point favourite in the 2016 Championship whilst also losing at odds of 1/3, 4/11 and 4/11 over the two league campaigns. They never won a game as underdog to balance these losses.

Is it predictive?
Although there are outliers – notably Carlow, Louth and Cork – I would lean towards no. There is just too much volatility as teams yo-yo up and down the table; Kerry from 27th in 2015 to equal 6th in 2016 and then back down to 28th in 2017; Cavan from 9th to 31st, Armagh from 32nd to 5th. Good luck trying to pick which of this year’s top5 will stay there!

Note1; calculating Expt Wins

Using the All Ireland final as an example. Paddy Power’s odds for the game were Dublin 4/9, Mayo 3/1 with the draw being 9/1. All that these fractional odds are is another way of expressing probabilities. To work out the probability any odds equate to you use the following formula (B/ (A+B)). For Dublin’s 4/9 the B here = 9 and the A = 4 so the probability of a Dublin win = (9/ (4+9)) which equals 0.692 or 69.2%. Do this for all three odds and you get

Dublin = (9/ (4+9)) = 69.2%
Mayo = (1/ (1+3)) = 25.0%
Draw = (1/ (1+9)) = 10.0%

The total percentages add up to 104.2%. Now we know that there are only the three outcomes for any game – team1 wins, team2 wins and draw – so anything above 100% for these three outcomes is the bookmaker’s margin. To get a truer understanding of the probabilities we strip out the margin equally across the three outcomes and come up with an Expt Win for each team. Dublin in this instance = 67.8% or 0.678 (69.2%-((104.2%-100%)/3)); Mayo = 23.6% or 0.236 (25.0%-((104.2%-100%)/3))

Note2; the odds
All odds are taken from Paddy Power and tend to be taken towards the back end of the week (Friday night/Saturday morning) to let any movements settle down. It is possible that injury news etc. changes the odds between what was taken and what they were at throw in but I’m comfortable enough that this would be a rare enough occurrence not to have too big an impact.