Promotion from the Conference

Gaining promotion from the Football Conference (formally Blue Square Premier League) is a hard business. No less than 12 former league clubs will be competing in the division next season and on top of this some ambitious clubs with great resources from the Blue Square North & South will be joining the other already established teams in there.

It has always been a very competitive division to get out of, Luton were expected to stroll out of the league and despite having average attendances far and above some of their rivals they have never been able to get over that final hurdle.

But what about the teams that do make it out of the division? As we have seen in the leagues above many teams have a tendency to yo-yo between divisions but this is hard to do when the Conference is so competitive, so it’s imperative that once you’ve been promoted you make a good stab at league football.

I used 20 years’ worth of data from Wikipedia to see how teams did once they’d been promoted from the Conference. The data goes back to 1989/90 but due to no teams being promoted between 1992/93 and 1996/97 and the introduction of 2 teams being promoted in 2002/03 I have data on 30 teams.

Flying High

Crawley celebrate winning a 2nd successive promotion

The first thing I looked at was how teams fared soon after being promoted

Very interesting that 14 of the 30 teams had been promoted to League One within 3 years of promotion from the Conference, with 8 of these going straight up through 2 divisions (Crawley Town the best recent example). Fleetwood are strong favourites to mount a promotion challenge this season just 1 season after promotion from the Conference and Mansfield are also expected to at least trouble the play-off spots.

So there is relatively little difference in standard between League Two and the top half of the Conference. Although the sample size is small the results are quite stark.

Unfortunately it does seem like League One is a step too far for most teams with 8 of the 14 teams coming back down within 3 years of promotion. This gap is reducing with Stevenage & Crawley both having a seasons and Yeovil Town – by now out of the range of our 5 years data mining by being promoted from the Conference in 2002/03 – surpassing themselves by being promoted to the Championship in last season’s play-off final.

Tough at the bottom

Macclesfield suffered the heartbreak of relegation in 2012

Unfortunately at the wrong end of League Two times are incredibly hard. The recent example of Aldershot, just 5 years after promotion to into the League and having already gone out of existence once in the late 80’s and now in administration again shows how tough any level of football is. If you aren’t pulling in big crowds you have to cut your cloth accordingly. The number of teams relegated from League Two who entered administration or went out of business is too high.

12 of the 30 teams promoted in the past 20 years have had some kind of major financial difficulty. Many of these have occurred when they have been relegated back to fifth tier level.

6 of these teams either went into administration or out of business completely and had to be reformed. Thankfully a lot of these historic teams are now on their way back up with the reformed teams of Chester City (originally promoted in 2003/04 and relegated in their 5^th season) and Halifax Town (originally promoted in 1997/98 and relegated in their 4^th season) both now promoted back into the Conference and Darlington winning the league in their first season.

With the level of professionalism now brought to the Conference, should it effectively be renamed Division Five (League Three??) The league is national, many teams are well back and professional standard, giant killings in the FA cup are less prominent and not as shocking as they used to be. The standard has definitely evened out in recent years.

What do the Stats show?

The final chart below shows the average positions for all teams competing in League Two in the years after their promotion for the conference (the average position of teams who were promoted into higher divisions has been removed).

The chart shows that the average position is around 11^th -12^th in the first season, quite a respectable finish and this improves in the 2^nd season and only drops marginally in the 3^rd. It is in the 4^th & 5^th seasons after promotion that the drop comes (a pretty steady average of 16^th in both seasons) and this matches up with the findings that no team has been promoted in the 4^th & 5^th season after promotion.

So if you are a Mansfield Town or Newport County fan you have plenty to be positive about, if you’re an AFC Wimbledon fan you’re in the last chance saloon for promotion and if you are a fan of Oxford United, Burton Albion or Torquay United you might just have missed the boat already.

Using Probability Theory & Poisson Distribution to win money!

I’ve been gambling casually on football for the past 8 years or so, and not making a great job of it! I’ve had a few decent returns but I’m almost certainly quite a bit down over the total time I’ve been betting.

Most gamblers will be split into one of a few camps. I do it to make a Saturday afternoon watching Soccer Saturday that bit more interesting (who was it that said it mattered more if there’s money on it?!), there are those that hope to win big and there are those that claim to be able to provide all the answers. I’ve seen a lot of these on twitter who claim to have xx% win rates – doesn’t really help having 90% win rate when you’ve picked a 10 team accumulator does it?!

Football is always a game of randomness and it’s so hard to predict with any great accuracy. My current method of gambling is partly between betting with my knowledge, partly through looking at the odds and seeing the teams with lower odds you’d think are good to include in an accumulator (not a good way of doing it as bookies have full control over the odds) and partly through statistics – things like form/league position/goals scored etc.

Why I’ve never decided to look more in depth at the statistical side I don’t know. Given that it is the area I am involved in I should have looked sooner but never really crossed the two paths of performance analysis and gambling until the past year or so.

After a bit of information gathering on the internet I settled on using Poisson distribution to look at previous scores and primarily the home and away goals scored by each team. (Chris Anderson & David Sally touch on this in their new book The Numbers Game)

So using the data from Football Data I have built a statistical model based on working a few things out and giving me the probabilities on a few things across the top leagues in Europe. The models cover 22 different divisions.

• England (Premier League, Championship, League One, League Two & Conference)
• Scotland (Premier League, Division 1, Division 2 & Division 3)
• France (Ligue 1 & Ligue 2)
• Germany (Bundesliga & Bundesliga 2)
• Spain (Primera Liga & Segunda Division)
• Italy (Serie A & Serie B)
• Holland (Eredivisie)
• Belgium (Jupiler Liga)
• Portugal (Primiera Liga)
• Turkey (Super Liga)
• Greece (Super League)

From all of these divisions the model takes into account the home goals scored and conceded and away goals scored and conceded (depending on where each team is playing) and using Poisson distribution and probability theory I can find probabilities of each of the following.

• Home Win
• Draw
• Away Win

• Home Win or Draw (Double chance results)
• Away Win or Draw (Double chance results)

• Predicted Score

• Both Teams to Score
• Both Teams NOT to Score

• Expected Goals Under/Over
• 0.5
• 1.5
• 2.5
• 3.5
• 4.5

Am I expecting to become a millionaire? No. But I am hoping that the model will greatly help me in my casual betting and so far I’m quite happy with it (I only began using it at the very end of last season and it needs a lot more testing – the hardest part has been waiting until the leagues start back up again!)

It is my aim to use this blog to provide a few updates of how it’s going and look to integrate other things into it, I’m mostly interested in how accurate it is at predicting the H/D/A and scores – the things with the highest odds and least likely to be predictable. For example if we pick a random game from the 1^st week of the Premier League Season – West Brom vs Southampton

• West Brom to Win – 50.28%
• Draw – 23.98%
• Southampton to Win – 25.74%

• Expected Score - West Brom 2 Southampton 1

Not clear cut by any means and just 1 very small example of what the sheet provides.

Hopefully I’ll provide updates on a regular enough basis to be interesting but not turn this blog into how I lost all my money gambling!

Do Goalkeepers Raise Their Game Against Big Clubs

Goalkeepers are traditionally very hard to analyse. There are understandably less statistics produced due to goalkeepers generally having less actions during a game, although the modern day goalkeeper needs to have added excellent distribution to his repertoire to effectively play as a sweeper. The limitations of the available statistics is probably for another post but the obvious one is whether a shot should be expected to be saved. Paul on his Different Game blog has already explored some excellent work on goalkeepers. 

Joe Hart was in unbelievable form during Manchester City's title winning season

From a throwaway line I heard on Match of the Day some time ago, I decided to investigate the impact playing against better teams had on keepers. In essence are performances like John Ruddy away at Liverpool for Norwich in securing a point reflective of their performance over the season or are they “one offs”.

Using the MCFC analytics data, which is now unfortunately a season out of date (here’s hoping they continue to release updates on a better than annual basis although the project does seem to have died a death) we have access to a full seasons worth of data using statistics provided by Opta. By working out how many shots each team face on average we can see whether the % they save is higher or lower against the ‘big 6’ (in this case I have used Man Utd, Man City, Chelsea, Arsenal, Spurs & Liverpool – sorry Everton fans!!)

In all 44 Goalkeepers played during the 2011/12 season for the 20 Premier League Teams and the first thing I looked at was how shots & goals affected the final league position.

It’s clear from the graph below there is a reasonable correlation between the number of shots faced and where teams finished (R Square is 0.6484). It’s common sense that the more shots teams have at you EVENTUALLY one is likely to go in, so conceding less shots initially should result in conceding less goals. Using Shots Conceded is effectively half of the Total Shots Ratio Model in reverse as with that model the percentage which you outshoot your opponent has a high relationship with finishing position – it means that if you concede less shots you are much more likely to be able to outshoot your opponent and ultimately finish higher.

Graph showing total shots faced vs final league position for 2011/12

The second graphic shows the number of goals actually conceded and is an even better fit, with an R Square of 0.7027. There are some outliers here (Sunderland conceded as many goals as Arsenal but there were 10 places between them – it’s OK not conceding many but you do actually have to shoot and score sometimes!)

Graph showing total goals conceded vs final league position for 2011/12

So knowing this I go back to my original point and whether some goalkeepers are able to raise their game against the big boys.

I chose a cut off of 1350 minutes (15 games) to look at, which gave me 22 goalkeepers. The gap between Thomas Sorenson (1350 minutes played) and the next 3 keepers in total minutes (3 at 720 minutes) was reasonably large anyway and a good place to cut.

First to look at is the total number of shots on target (excluding penalties) each goalkeeper faced. We can see from the simple table below which keepers immediately stand out.

Minutes per shot on Target

Minutes per Goal Conceded

Minutes per Goal conceded vs Minutes per Shot Faced

Immediately Joe Hart stands out as much better than the rest (remember, this is 2011/12 data so his dip in form last season is not taken into account) whereas at the other end Adam Bogdan and Jussi Jääskeläinen are both in the bottom 4 keepers in terms of the lowest number of minutes per goal conceded. This goes some way to explaining why Bolton were relegated.

So where it was taking just 2.5 shots on target to beat Paul Robinson it was taking twice this many to score past David de Gea. Not bad for a keeper who supposedly had a poor first season!

David de Gea is now proving to be the top class Goalkeeper he was signed to be

I’ve now compared the figures against the previously mentioned big 6 against the other teams. For this I have removed Sorenson, Jaaskelainen, Begovic and Bogdan due to them not playing enough games against the better teams. I set a minimum of playing at least 2/3rds of each of the games against the big 6 and the other 13 to give a reasonable representation.

Minutes per shot faced - Big 6 vs Other 13

In all cases the big 6 shot on target more frequently than the other 13. This was pretty much to be expected as you would expect the better teams to produce goal scoring opportunities more frequently than lesser teams.

Shots per goal conceded - Bg 6 vs Other 13

This is where it gets interesting, there is no clear distinction showing keepers raising their game against better teams. Michel Vorm, Simon Mignolet and Mark Schwarzer all have excellent records against the big 6 and along with Paddy Kenny all actually conceded less goals per minute against the better teams than the others in the league.

Joe Hart is also in there, and although his record is good in both cases it’s better against the better teams, maybe a lack of concentration in the less important games due to being so well protected?

David de Gea and Brad Friedel come off worst, de Gea’s phenomenal numbers against the other 13 teams (Man Utd always seem to have an exceptional record against teams lower down the table) was always likely to drop against the better teams, but it remains as one of the better ones in the league. Brad Friedel shows a big drop and maybe the 5-1 home hammering Tottenham took by Man City (remember Dzeko’s 4 goals??) from only 9 shots on target skews the data and shows the fragility of how one result can affect this.

Unfortunately these statistics only show the tip of the iceberg. Is there a reason for keepers performing better against better teams? They are likely to have more defenders in the box making the shooting opportunity not as clear, maybe they take shots from further out. All this is speculation and until further detailed information is available speculation is all we have.

Another goal flying in past Paul Robinson