Player Comparison Charts – League of Ireland

Yesterday I released a chart examining players in the League of Ireland. As far as I know, this kind of work is new to the league, though correct me if I am wrong. After all, I might have missed something along the way with my head buried so deep in Excel and Tableau. There is a possibility that some club analysts may already be using these charts for recruitment purposes. Although I do find that unlikely. You’d be surprised how many Premier League club’s don’t do this yet either. Using these kinds of dashboards to convince coaches and chairpersons that the player they are pursuing is a fabulous visual aid. So without further ado, I have attached the visual comparing two strikers below: Cork City’s Graham Cummins (30) and Dundalk’s Patrick Hoban (26). That picture of Cummins is deceiving…he looked mid 20’s to me. 
Dashboard 1

Data Collection

So let’s get started. First and foremost, the data collected comes from Instat. They cover a wide range of leagues and players worldwide and data is collected on a massive scale. I do not have the slightest idea how they collect their data as they often require a video feed to analyse the games (or so I think at least). This requirement for a video feed can often lead to below par video footage (which I myself have) often seen on occasions. Either how they collect a bunch of metrics which can be seen in the visual above. The number of metrics is not particularly the issue, rather the quality of them. Firstly, I personally am more familiar with Opta’s data and therefore trust it’s reliability and it has been used more often in the public domain (that is not to say that Instat’s collection is wrong, more than it needs validity and reliability tests). Secondly, the quality of metrics is not as in-depth as I would like. For example, the visual above shows Shots & Goals. That is great as a start but we would like more granular information, such as shot locations and expected goals. These are now considered the norm in evaluating players, even though they do have their own problems.

As for the definitions of the metrics used, Instat does not provide these. I assume most are self-explanatory – ball recoveries is another way of saying Interceptions?. Where Tackles and Challenges differ is another world to me..

Player Analysis

Back to the visual itself. The easiest way to “read” the visual is to examine each “box” and identify the dot that is furthest to the right. The visual is designed to compare two players against each other at the same time. In essence, the further to the right the dot is, the better that player is in that metric. Important to remember that not all games played are included in the dataset. The reason being that the data provider has not covered all games. The easiest and most effective way, therefore, is to examine players on a p90 basis. Please read through p90 before continuing below.


Let’s take a look at the snapshot to the left. While Cummins has played fewer games and took fewer shots than Hoban, he has a higher goal per game ratio than Hoban. Cummins on average also provides assists to his team more often than Hoban. Is he, therefore, a better striker? I do not know the answer to that question. What we can establish from the data is the following:

  1. Cummins may be more effective in converting shots to goals. Yes, I said it! That totally depends on several things: his shot selection, his age, his playing style and a few other factors – currently which I do not have access to. For example, if we had access to shot locations, we could establish an expected goals total for the two players and compare their actual vs expected figures. This may help identify a “better” shooter.
  2. Or the season is still too early and doing any of this work is just plain nonsense. Agree, with the first part. It is still very early on in the season and a small sample size is a big plague (for now). Let’s revisit this towards the end of the season.

Sadly, that is all the data I have access to at present. The metrics, for now, are most likely benefiting the attacking rather than the defensive players. It is a small side project that I run out of enjoyment and a personal goal of adding analytical value to football in Ireland. You are more than welcome to ping me and request player comparison graphs for the League of Ireland and I will post them on Twitter. That’s all for now folks.

Year Ending 2017 / Starting 2018

To all my followers and the newcomers, you may have noticed that it has been a while since this blog was “updated”. That was due to several other exciting analysis opportunities that arose towards the end of 2017. If I remember correctly the last piece of work I wrote on this blog was a basic scouting comparison report of finding a replacement for Chelsea’s N’Golo Kante (if he was to leave). Using data I had gathered I was able to assess two players’ who are also quite unique like Kante.

Most of my work since then has either been confidential for clubs in different sports or more visually orientated– designing Dashboards and understanding “The Best Practices”.


I furthered my knowledge of Tableau by gathering, analysing and presenting data through interactive visual dashboards. This included an interactive CV (full version) and a tutorial how to map XY co-ordinates in Tableau among other things.

  • The Premier League Scout


2. Major League Soccer (MLS) Shot Maps (Teams & Players)


*If you are looking to learn how to embed Tableau workbooks into your blog, this post is great.

August 2017 – present

• I formed an analysis group with fellow GAA county analysts and coaches. We provided analysis support to clubs during the club campaigns around County Dublin & Kildare.

• Currently, working remotely for a soccer club in the Middle East providing opposition & data analysis support as well as helping with player profiling & recruitment.

• I continued basic xG (Expected Goals) metrics for the German Bundesliga and South African Premier Soccer League & a post-game Dashboard.

• I continued another passion of mine which is traveling.  I ticked Birmingham, Cardiff, Edinburgh and Swansea off my list while re-visiting Berlin and Stockholm. Most recently, I went back for a family trip to South Africa also.

• In January, I attended the English Institute of Sport’s (EIS) Skills4Performance workshop which was very beneficial in terms of further developing communication, organisation, professionalism and critical-thinking skills.

• I joined a provincial county football team providing analysis and analytical consultancy for pre-match, match-days and post-match.

• In February, I (alongside two fellow analysts – Ray Hamill (@FinerMargins) & Ferdia O’Hanrahan (@FerdiaOHanrahan) presented at the OptaPro Forum in London. We discussed using Opta and ChyronHego Tracking Data how teams’ can “Develop optimal defensive strategies for corner situations”.

That’s been it for so far. The plan for the rest of 2018 is to:
1. Learn new and develop knowledge of existing software (NacSport, Python, SQL, Excel, Tableau).
2. Travel
3. Enjoy life
Thanks for reading.



An Examination of Corner Kick Strategies in European Leagues


Before proposing to present at the OptaPro Forum this year, I had considered corner-kicks (and still do) a very interesting topic in football. While fascinated by the topic, corner kicks are at times quite repetitious (inswing/outswing) with short corners thrown in occasionally, for a bit of variety. The questions I put forward for the Forum were 1) To examine if the different leagues in Europe preferred certain corner kick strategies over others and if so, which teams and 2) Were some areas more dangerous to deliver a corner kick to than others?

The Data and Process

Using corner kick event and qualifier data from the 2015/16 season of Europe’s Top 5 Leagues (Premier League, Bundesliga, Ligue 1, La Liga & Serie A), I went ahead with my analysis. After running some initial tests, I had to conclude that I would not be able to solve corner kicks in one attempt over a six-week period. So instead, I focused my attentions to the first phase of play (the delivery from the corner) and the events/actions that occurred during this phase. The sample size of corner kicks for this study was 18,425.

Screenshot (328)Using Opta’s F24 files, I first had to connect the event data with Opta definitions. On completion of that, I next used the XY co-ordinates to break the opposition half into zones. I decided on 11 zones (left), which are divided as follows:

Inside 18-yard box: 2 – 9     Danger Zone: 3, 4, 5, 8


The Results

I won’t go into the smaller details of the poster. You can read the different types of corners used by each league and number of goals scored by each corner in each league. What I wanted to bring to the attention of readers was the conversion percentage of corner kicks and zonal conversion rates for each area. For the 2015/16 season, a corner kick was converted at around 3%. Zonal conversion rates differed in each league and zone, yet averaged close to 3%.

Obviously not every team used the same corner kick strategy and the types of corners in different leagues was different. Therefore, I don’t want to give any direct take-a-ways based just on corner kick conversion rates. There was another aspect that I thought would be able to give a broader answer.

Successful Passes

Looking at successful pass percentage rates from a corner kick, we could establish more varied results between zones and therefore give an idea of what zones are more successful to direct a corner to. A successful pass in this instance was defined: “a pass that reached a team-mate”. Naturally, I did not fully examine this per league as the sample size would have been too small.

For the Forum, I designed an interactive element through Tableau which allowed me to visualise each teams’ corner kicks and the zone the kick (first phase of play) went to. I can’t publish the visualisation because the data doesn’t belong to me, yet a few samples of the work are shown in the video. Make sure your sound is turned on (I’m narrating).

On the Day

I very much enjoyed the stand-up talks as well as listening to the other poster presentations. As for my poster, I was delighted with the interest that it received on the day. I had some interesting conversations with people from all areas of the football world, asking about my work and my take on corner kicks in general. In this next part, I will discuss some questions I received on the day and any future analysis that should be studied on the topic.

  1. Where was the interest from and what did they want to know?

I met so many people that day but if my memory serves me correctly, most of the interest came directly from club analysts. Nearly everyone wanted me to answer: “How does one score more goals from corner kicks?”. Unfortunately, I did not (and still don’t) have that answer on hand just yet. Corner kicks are such a difficult aspect of football to analyse, probably the most difficult due to the number of events that take place within a time frame (10 seconds for Opta coding). Yet through a first stage analysis (such as the one I undertook here) teams can use this as a method to visually analyse opponents first phase of a corner kick strategy.

  1. Why divide the opposition half into those areas?

Measuring out the zones from co-ordinates by hand themselves proved very tricky, especially the closer to goal I came. Therefore, the pre-measured XY co-ordinates in the F24 files from Opta played a big part in deciding how the zones were divided. While this left some zones (such as 7, 8 and 9) bigger than others, I felt it reduced the possible error count significantly.

Future Analysis

There are a few pieces that should be undertaken as further research projects within the realm of corner kicks. Mainly being:

  1. Phases of play and building a model

The next steps should be to try and incorporate the other phases of play and build a model to examine corner kicks in more detail. What detail that should include will be up to the individual, yet they should incorporate attacking and defensive tendencies. For instance, not all corner kicks are scored in the same way and therefore this should be included in the next stage of analysis.

While not all in/out-swinging corner kicks are scored straight from the first phase of play, short corner kicks will need an extra phase of play to even reach the Danger Zone, which allows teams to re-group for the likes of a cross for instance.

  1. Tracking Data

Including tracking data such as Martin Eastwood’s poster presentation write-up last week, can help with added decision-making and optimising chance creation.

  1. Game State

Examine the effect that Game State has on corner kick strategies. For example, do teams have a strategy for the game and if losing, adapt that strategy to try something different (short corners due to time constraints)?

  1. Defensive strategy

We often seem obsessed with measuring attack before defence in any new metric. While I did the same with corner kicks, the defensive aspect of how to defend corner kicks, would also be beneficial for teams to examine. I hope that this poster/blog post is of interest for teams to examine where their next opponent delivers the ball to. From there, using video how can we stop them creating/scoring chances from a corner kick?


My thanks go out to Ryan Bahia and Tom Worville, who were extremely helpful when explaining the different data event points to me.

*My poster can be viewed below.

Alex Rathke OptaPro Forum


Introducing Player Visualisations for 6 Nations Rugby

Since I joined Twitter in March 2015, I have seen numerous visualisations ranging from the work of #MakeOverMonday (who build visuals every Monday on a range of topics) and individual bloggers. There has been some fascinating work in the public realm such as Ted Knutson’s player radars and Neil Charles and his OptaPro Forum visual from Feb 2015. All have been very engaging and I can definitely say that I have a learned a lot even just by seeing other people’s work.

In this blog post, I would like to try and 1) understand why other sports are (possibly) lacking behind football in publicly available player metrics for fans (and coaches alike) and 2) help bridge this gap somewhat by introducing and developing player metrics for rugby. The work on Statsbomb and American Soccer Analysis have probably been thus far the two most resourceful website for work being done in football analytics (though there are also individual bloggers out there) . In terms of other sports such as rugby, hockey, cricket and netball, there seems to be a lack of platforms for analytical blog posts and discussions on topics in those sports. One company that I have seen who publish quite a number of quality articles in rugby are OptaPro. After that though, that’s as far as a platform goes. Maybe I am wrong and haven’t looked hard enough through Twitter and the general internet to find these platforms? Could it also be due to a lack of data available for different sports?

While I am on the topic, I thought I would also quickly talk about visualisations. Over the past while, I have seen numerous visualisations produced on a such a rapid scale, yet the story they are supposed to tell are rarely discussed. Obviously, we as viewers can make our own interpretations, yet we might interpret the visual/findings wrong. Visually we see the actions but discussion is lacking in an accompanying/ follow-up blog post.

Visualisation & Key Performance Indicators (KPI’s)

I had some trouble – or let’s say a lot of trouble finding rugby stats. Some websites only gave me kick percentage which was anything but insightful, while others provided some but absolute minimal data of any sort for club level rugby. This blog post might have never materialised if I had not stumbled across the New Zealand Herald. Thanks to them, I was able to use the publicly available data provided by Opta (I assume – as their logo appeared at the end of the page). While the statistics are not as detailed as I would like them, I am quite happy from what I was able to gather together to use as metrics for player evaluation and/or benchmarking. In no particular order, I was able to collect the following statistics:

• Minutes Played   • Carries   • Clean Breaks   • Defenders Beaten   • Metres Made   • Offloads   • Passes

• Points   • Tries   • Tackles Made   • Tackles Missed   • Turnovers Conceded   • Open Play Kicks   • Lineout Catches   • Lineout Steals

One thing I will mention before discussing the metrics in a bit more detail is that I was unable to find event definitions that Opta use to code the metrics above. The football events definition list is very easily accessible but the rugby one seems to be kept within the secret walls of Opta (which is totally their right to do so). Though the understanding of these events is then difficult. How are the events coded? What is the subjective input for the objective observation? Anyways, as you’ve read through the list, there are a number of things that automatically spring to mind.

  1. Good overall metrics to assess performance but quality within each metric is missing.

Passes for example….we don’t know if it was successful or not. I have seen rugby clubs even further distinguish these through descriptors as a positive or negative pass for an individual player. The same process can be applied to Tackles (above).

2. Of the quality, which are more important than others?

Over the course of a season, which metrics may tell us more about players as Expected goals have been able to for players and teams in football.

3. Off the ball events are still and will for a while be re-occurring issues. Sometimes just like video alone, statistics cannot tell us everything we might need/want to know about a player.

I am pretty positive though that this niche has already been exploited and this “tracking data” as it’s known is slowly (if not already) making it’s way into clubs and international set-ups. ChyronHego are one of the solutions if you are interested.

Getting access to more detailed data can be expensive. As for a student and amateur bloggers, time will be the biggest drawback. I don’t personally have an idea how much companies like Opta and Stats sell data for but I can only imagine that this data is sold in packages (with each package having more exclusive event details and therefore costing more dollars).

Overall though (as I have already mentioned), I am very happy with the data that I was able to find and therefore I think will give some useful insight for all rugby enthusiasts involved in the game.

In order to break the data (KPI’s) down into more manageable chunks, I divided a squad into 10 positions:

• FlyHalf   • Prop   • Centre   • Locks   • Hooker   • Wing   • Fullback   • No. 8   • Flanker   • Scrum-Half

and then attributed the metrics in accordance with player positions. Even though players have a set-position when they start out, these can change over the course of the game or even over a tournament or season because of a number of events: 1) game needs – such as tactical adjustments or through 2) injury to players and so forth. For example, Ireland’s Rob Kearney sometimes plays as a Fullback or Winger for Ireland. There are then two ways this visualisation can be used.

  1. If he plays one position for a couple of games, his output could be measured against that of other top level rugby players over the course of a couple of games/season in that position.
  2. If he however is used constantly between different positions on a rugby pitch, these charts will not a) only become slightly harder to read, they b) will give a skewed output on statistics and c) his performance could still be benchmarked against what other top level rugby players are doing in that position.

Another problem that I quickly noticed was that players played a variety of different minutes throughout the 6 Nations tournaments. For the visual that I designed, I took some aspects from the football analytics community (very active on Twitter) and applied them to my visualisation. The main aspect being p90 which was introduced to us by Benjamin Pugsley was adapted to p80 for the duration of a rugby game.

** Please have a read over p90 before moving on if you are not familiar with it. It is very important to understand how it works and why it should be used when evaluating players in all sports, not just football. **

The one hindrance this dataset has is that usually one would like a bigger dataset to work with. Seeing as this though is an introductory piece and the 6 Nations tournament does not have that many games, we can use it as a sort of trial run. Ideally, I would like to use this to analyse players in a full rugby season, but the 6 Nations Tournament will have to do for now. For the visualisation, I have only included players who played more than 2 p80’s during the full 2015 & 2016 6 Nations tournaments.

How to Read the Visualisation

Now how do I use the chart and more importantly read it?

Let’s take this example of the 2016 6 Nations tournament and look at three Flyhalf’s metrics (England’s Owen Farrell, Ireland’s Jonathan Sexton & Wales’ Dan Biggar). I’ve decided that I would like to first show the visual results before discussing the results in writing. The further right the player’s dot is, the better they performed (except for Tackles Missed & Turnovers Conceded) – where the opposite applies). For the Flyhalf position, I looked at the following metrics and will discuss each metric separately below.

• Minutes Played   • Penalty goals   • Conversions   • Points   • Tackles   • Tackles Missed   • Offloads   • Open Play Kicks

So without further ado please find the visual below. Are the results surprising and if so, where and why?

Screenshot (323)1. Minutes Played

All three played at least 3 p80’s minutes which really means they played this many minutes (Farrell – 381, Sexton – 349 and Biggar – 297).

2. Penalty goals

While Farrell scored more penalties than Sexton and Biggar per 80 minutes on average, I would not call the numbers significantly better than his counterparts. If anything, kick location using video should be used here to further develop a profile on their kicking ability and if one or all three possibly have a difficulty with a certain area or angle of kick.

3. Conversions

While Biggar’s conversion numbers for the 2016 tournament were better, this could be down to Wales scoring the most tries (17) followed by Ireland (15) and England (13). Therefore, Biggar had more chances to convert the tries than his counterparts.

Again (just like the penalty goals), looking at where the conversions took place would also influence if Biggar was indeed better at converting his kicks than Sexton and Farrell.

4. Points

The graph only shows two data points (those of Biggar – orange and Sexton – green) but Farrell’s purple dot is nestled in neatly behind Biggar’s. The reason for this is that Wales scored the highest points total (150) of the tournament followed by England (132) and Ireland (128). On a per 80 minute scale, Biggar lead this category for the tournament (14.55) followed closely by Farrell (14.49).

5. Tackles and Tackles Missed

Based on these results, we can see that Farrell and Sexton were more involved in committing to tackles on average over an 80 minute period (9.87 & 9.63) than Biggar (7.81). On the tackles missed per 80 minutes, Farrell missed the most, Sexton the least and Biggar was in the middle of the two. Yet if we subtracted the tackles missed from the tackles, Sexton would have come out on top in terms of efficiency (8.25) followed by Farrell (7.35) and Biggar (5.93) on average over 80 minutes.

6. Offloads

The number of offloads made by the three Flyhalfs on average per 80 minutes were limited to around one or none a game. Biggar made more offloads than Farrell, while Sexton did not attempt any. Offloads are a risky form of pass as loss of ball control can happen at any stage but can therefore be very effective in forming attacking play.

7. Open Play Kicks

Open Play statistics have been thoroughly examined throughout academic research and have been mentioned to distinguish winning from losing teams (Bishop & Barnes, 2013) & (Villarejo et al., 2015). For the purpose of this blog post, open play kicks (I assume due to the name) are kicks made during the game an excludes conversions and penalty goal kicks. Biggar (16.97) again excelled over Sexton (13.52) and Farrell (5.25) in open play kicks per 80 minutes. These numbers for Biggar and Sexton seem to be quite high and while I cannot find any numbers for direct comparisons with other Flyhalf positions, winning teams according to Villarejo et al., (2015:107) “completed significantly more kicks” and Watson (2016:2) average “around 30 kicks per game” (out of hand). This amount would (over the course of a game) seem to be accurate, as Flyhalf’s make a number of key tactical decisions (whether it be kicks or passes) which will impact on the game.


Overall, we could say that Biggar had a better tournament than Farrell and Sexton in a FlyHalf position. This could be put down to his conversion, points, tackles, offloads and open play kick metrics, which were all higher even though he played less minutes during the tournament. However, it’s not that simple to be honest. As I like to put it, the above metrics paint a picture (mostly still black and white to an extent) of which players may be better at some metrics than others, the picture is still missing a lot of colour! Just because the statistics and metrics may favour a player (in this case Biggar) based on his 2016 performance, there are still a lot of other/unknown factors accounted for. Deeper analysis is needed to paint the best picture possible of a player/position.

How to use the visualisation

Now you know, who’s better at this and that (the old Cristiano Ronaldo vs Lionel Messi argument), let me quickly talk through how to use the visualisation.

In terms of using the Tableau workbook online, it is free to access! I would hope that it is of benefit for the development of rugby, as visuals have been for the development of other sports and even business in general.

I designed the Tableau document as fool proof as possible and the filtering actions on the right-hand-side should hopefully make sense. In case they don’t, they allow you to filter individual player, country and even year on year performance for each position.  Click here to access the visualisation (xxx). Else

Elsewhere, time and memory dependent, I would like to update the workbook after the 6 Nations and then discuss some player specific visuals or even position specific visuals.

I look forward to hearing about any feedback that you may have regarding any part of this blog post.



P.S. I would also like to thank @john_farrell90 for his insight and help.


• Bishop, L. & Barnes, A. (2013) “Performance indicators that discriminate winning and losing in the knockout stages of the 2011 Rugby World Cup”. International Journal of Performance Analysis in Sport, 13: 149 – 159.

• Villarejo, D., Palao, JM., Ortega, E., Gomez-Ruano, MA. & Kraak, W. (2015) “Match-related statistics discriminating between playing positions during the men’s 2011 Rugby World Cup”. International Journal of Performance Analysis in Sport, 15: 97 – 111.

• Watson, N. (2016) “Exploring data analysis in rugby union”. OptaPro.

Introducing Box Key Passes for the German Bundesliga

After match-day thirteen of the 2016-17 Bundesliga season, a number of things have been confirmed to us:

  1. RB Leipzig have taken the league by storm playing a young side or more like the youngest side in the league this season.
  2. Pep Guardiola’s effect on Bayern has been noticed almost immediately with Carlo Ancelotti’s more relaxed playing and managerial style.
  3. The fight for bigger Bundesliga clubs (this year: Werder Bremen, Wolfsburg and Hamburg) to avoid relegation is a yearly occurrence and continues to provide entertainment to fans worldwide.
  4. Giving Julian Nagelsmann the Head Coach position at Hoffenheim at the young age of 29 has been nothing short of a success.
  5. Frankfurt have certainly turned the corner with Nico Kovac at the helm and extended his contract until the end of the 2019 season.
  6. And lastly FC Cologne have scored 18 goals of which Anthony Modeste has scored 12 alone, that’s a crazy 67%.

Elsewhere, (before I get moving onto the topic of the post) Hertha Berlin and FC Ingolstadt are on literally opposite ends of the league table in comparisons to their GD-xGD. This basically means that their xG performances have put Berlin over-performing or being lucky (along with other unaccounted factors that are not included in my model), while Ingolstadt are the opposite. I will briefly discuss this in more detail below.

So, as the title of this blog post mentions, I want to use this opportunity to introduce a “new metric” called ‘Box Key Passes’. What do I mean by this? Before I talk about them, let’s take a short history lesson on passes and key passes. What are they?


Opta, one of the big football data collectors and providers define a pass as “An intentional played ball from one player to another”. Now there’s already an issue with this statement is there not? There is no mention of clubs/teams or more specifically team-mates…so are passes just being played around the pitch? No, well yes they are but more detail is necessary for us to understand what is going on (see image below). Obviously, the coders coding the game will know the difference (as will everyone else who watches football), yet it is the simple details in the definition that can cause problems. It’s instances such as these that do not help us performance analysts portray acquired knowledge to coaches correctly. It is not a good start to have coaches doubt you (even if you are indeed just trying to help them). But that’s a story for another time and place.

Ok, but we still don’t know what a Key Pass is mate…!?

Oh yes, apologies, I got a bit carried away. Key Passes by Opta are defined as: “The final pass or pass-cum-shot leading to the recipient of the ball having an attempt at goal without scoring” (although Colin Trainor with this Statsbomb piece mentions a Key pass as “regardless of the shot outcome”. Delving into further detail reveals Second Assist/Key Pass: “A pass/cross that is instrumental in creating a goal-scoring opportunity, for example a corner or free-kick to a player who then assists an attempt, a chance-creating through ball or cross into a dangerous position”.

For the purpose of this blog post and if we ever have a chat about this topic, I refer to Key passes as the first quotation with scoring rather than without. Two reasons:

  1. They are basically named the same (both record events leading to a shot)
  2. (Opta data supplied) say “Key pass with an assist), so there is no need for the “second assist/key pass” definition.

And now for the ultimate……drum-roll, please…..Box Key Passes.

Essentially for this metric, I counted up the number of key passes that start outside/inside of the box but end inside the box. I wanted to examine the Bundesliga data that I have for this season and test out this metric.

Before talking about some correlation values, let’s have a look at the passing and key passing numbers of all Bundesliga clubs after Match-Day 13:

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The table on the left shows the average number of passes made by each team, while the table on the right shows the average number of key passes made by each team. Some teams may differ a lot due to their playing style, while others may not be able to find that through-ball to get behind the opposition defence. Let’s take a couple of examples. High flying RB Leipzig are mid-table for average passes per game but jump to second when we look at average key passes made per game. Leipzig want to play fast forward moving passes and this blog post by Dustin Ward who can be followed on Twitter looks into them in more detail. Elsewhere, I talked about Hertha Berlin through this vlog and mentioned their lack of Box Key passes and shooting numbers as crazy to comparison where they were/are in the league table. In early Dec, Jack Grimse also wrote about Hertha for Paste Magazine).

Correlations and coefficient of determination:

I am not the first person to have looked at possession stats and nor I probably will be the last. This bold piece on possession by Ralph Honigstein ( is well worth a read on how possession is still quite relevant these days. It’s all about trying to figure out what the numbers 1) say and 2) don’t say and communicating this to the key decision makers.

I have been intrigued by Key Passes for some time now and wanted to examine them in further detail. Below are some correlations – r (coming as close to 1 would be a perfect correlation, e.g: as passes go up you would pick up more points) and coefficient of determination – r² (examines the variance in points acquired and goals scored based only on the number of passes, key passes & box key passes) that I ran from the Bundesliga data I have for this season.

Correlations (R)Points Goals Scored
No. of Passes0.520.58
Key Passes0.610.67
Box Key Passes0.590.6

R squaredPointsGoals Scored
No. of Passes0.270.34
Key Passes0.370.45
Box Key Passes0.350.36

I have also included a Tableau graph of Box Key Passes vs Goals Scored for all Bundesliga teams below.


Anything interesting jump out?


  1. Looking at the correlations statistics, the amount of passes a team completes with returns to points and goals scored is only moderate (0.52 & 0.58). This is fairly obvious because endless passing with no penetration for going forward does not help you win matches or score goals.
  2. I am surprised that Key passes had a higher correlation with points and goals scored than the newly introduced Box Key passes. While it’s not better by a landslide and only by a few decimals, how could this still be?

• The closer you are in the opposition’s half, the better chance you have of creating an opportunity to score. Obviously, there are still countless obstacles in your way, such as a defensive system, the number of players and the distance from goal but you are already in the opponent’s half of the pitch. Seeing as Box Key passes only take into account the number of key passes that are played into the box, the correlation to goals scored should be higher than just general key passes (or so I would have thought).

• The issue at hand could also for the time being just be a simple matter of time and sample size. Technically speaking only 38% of the season has been played so far. By this I mean that both elements (time and sample size) have been affected (so to say) and there is just not enough data available yet at this stage of the season. So while, the number of passes will always be high, the other two variables occur less and less due to the nature of the game (Box key passes depend on forward movement by teams).

Coefficient of determination (R²) also deserves a quick look over to see what/if the results can tell us anything useful. No. of Passes again was on the lower end of variance for both points (27%) and goals scored (34%). Key passes again was more superior than Box Key passes for both points (37% vs 35%) and goals scored (45% vs 36%). Come to think of it though (after 13 weeks of Bundesliga action), 37% & 45% of the variance in points and goals scored can be explained by Key passes. It is still way too early though to draw any conclusive conclusions and therefore, I would like to examine this blog post again in May when the Bundesliga season has come to a close.

Otherwise in general news, I currently work as a performance analyst at a boarding school in North Wales. In my spare time, I collect and work with Bundesliga data with Tableau. Otherwise, I am active on Twitter where you can follow me. I hope you have enjoyed this blog post about Box key passes and if you have any feedback, I would be really keen to hear it.


Alex Rathke

Performance Analyst

Life as a Performance Analyst in Major League Soccer

I have been an Intern in the analysis department of the Houston Dynamo under the very knowledgeable Oliver Gage for the past five months. If you haven’t heard of him before, he’s active on Twitter or can be found through a simple Google search. He’s well known and very respected in the industry for his work, both in public blogs and presentations and private analytics (club work).

These past 5 months have been amazing and I can say with confidence that I have learned a lot under Oliver. Especially how the ins and out of a professional football club function on a day to day basis when considering performance analysis and statistics. In this blog post, there are a number of areas that I would like to address which will hopefully give fans and aspiring analysts an insight into what to expect in MLS.

1. Major League Soccer (MLS)

What is the league all about, how does it work and how good is it?

2. The work of a Performance Analyst

What do we actually do? Why are we wanted/needed?

3. Weekly challenges in MLS

Weather, Travel, Games & Finances

4. An example of a day in the life of an analyst

Major League Soccer (MLS)

One of the reasons I choose to go to the USA instead of the UK (where most of the job experiences and openings are) was because I longed for something different, I wanted to stand out to future employers. Oliver and the Houston Dynamo also gave me the option of deciding how long I wanted to stay for which was a huge benefit, especially as I also had to plan finances. Other benefits so to say included a smaller analysis department, thus allowing me to learn, assist and add ideas to the club. This was definitely something I don’t think many other aspiring analysts, especially in the UK would have had the chance to experience.

As some people might already know, MLS is quite different to  the Premier League, La Liga or even the new money crazy Chinese Super League. How different? This video explains it better than I ever could (and also saves me explaining every detail in writing). If you fancy learning about MLS’s history I would recommend “The Beckham Experiment” by Grant Wahl. It’s a good introduction.

First and foremost, I would argue the biggest difference is what is known as the salary cap. There are of course other factors that affect the teams such as travel, weather conditions and turnover of games (as they always do). The league recently became more flexible after introducing the Designated Player (DP) rule in 2007.

Hopefully, the first YouTube video answered all of the questions about MLS and how the league is structured. Teams are distributed to a conference as can be seen in the left image below. While I was in Houston, MLS confirmed that next season (2017), there would be a further two additions to the league: Atlanta and Minnesota United.

The work of a Performance Analyst

So what does a Performance Analyst actually do? Let’s ask some family and friends…


In some ways the above meme isn’t completely true but it’s also not completely false. We do watch a lot of games (for obvious reasons) but there are numerous other projects that we take on. Sure it’s all about those valuable three points is it not?

Since being here at the Dynamo, I quickly learned that our job is still largely misunderstood by a lot of coaches. Nothing against the coaches but I can understand their perspective (why do this?). Essentially, we support the coaching staff with pre, live and post-match analytical information (both video and statistical).

Matthew Lewis of Bristol Rovers explained it as such: “Performance analysis can be defined as many things and be used in many different contexts but ultimately it is the in depth critical analysis of a set performance, and being able to dissect various elements of the game to the clubs’ and coaches’ needs”.

This information could be anything. That reminds me of the quote from William Bruce Cameron “not everything that can be counted counts, and not everything that counts can be counted “. However, we try to provide anything the coach wants to know about his team and the upcoming opposition. Some coaches feel the need to want to know everything while others are flexible and don’t particularly want an IO (Information Overload).

Generally, most of the analysis (pre & post) is video based. This is due to data reliability issues as small samples cannot provide us with such accurate information as quickly as video can. Statistics are more useful in the long-term to use either for benchmarking performances or finding that “needle in a haystack player”. Elsewhere statistics can have an important role to play but NEED to be used correctly (otherwise they can be misinterpreted and could lead to some seriously bad decision-making)!

If you follow football bloggers in the football analytics community on Twitter you will have seen this tweet by Colin Trainor before. He’s quoted this statement directly from Prof Bill Gerrard’s blog “Winning with Analytics”.


Just this week, my supervisor Oliver described an encounter he once had at work and wrote a piece on the current state of analytics. Again, it’s worth the read!

My Role

During my five months in Houston, I worked on a number of exciting projects. I won’t go into all of them in detail, yet I have chosen five that I feel helped my development as an analyst the most.

1) Build upon and develop the academy analysis system in accordance with their style of play.

2) Daily filming and coding of Training sessions.

3) Help with general pre-game opposition analysis (video and statistics).

4) In charge of opposition set-piece analysis.

5) Produce and write regular reports for the coaching staff, e.g.: goalkeeper coach.

For home games, we figured out a way to capture live off a camera instead of using the TV feed. Don’t get me wrong, there’s nothing wrong with the TV feed (it’s better than having nothing)! Yet, being able to provide a professional service with whatever you do goes a long way.


img_7847Due to the sun, heat and humidity that Houston receives during the summer months, it was enjoyable for a change to stand outside and film training from the roof of the training facility. Certainly very different to back in Ireland and the UK with the rain-jackets, camera protectors and so forth.

However, once up there I got my daily dose of Vitamin D with temperatures hitting 38 – 40 degrees Celsius (plus humidity) and this view. Definitely better than any cold and rainy day back home!



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Role restrictions

As with everything in life, there are restrictions. Restrictions that can hinder personal or career development. Unlike the big clubs in Europe, performance analysis is relatively new to MLS. As far as I know of right now, most clubs (in MLS) currently only employ one analyst. This makes for long hours at a laptop, preparing footage and other pre-game visualisations the coaches want to see. Even for two analysts per club, the workload would still be intense but more manageable. It certainly is not a job for the average football fan. You really have to enjoy the work and the hours that come with it!

screenshot-112One week in the midday July heat, Oliver and myself decided to pull the scaffold over from the academy pitches (picture below – red dot) to the First Team pitch (green dot). The scaffold would allow us to be closer to the pitch but it ended up not being tall enough for a quality shot. (On a side note, I probably should point out that it was some effort pulling and pushing the scaffold over freshly watered grass. If I ever thought about entering the World’s Strongest Man competition, this would be a good exercise to start with).

Weekly Challenges in MLS

 As I discussed earlier in this post, MLS over the course of the season faces a number of challenges (long travel to away games, weather conditions and a short turnover of games). These factors have to be taken into consideration when scheduling and planning for the season ahead. Let’s first take a look at the weather conditions that teams can face when travelling to away games in this league.


There are a number of different weather conditions that MLS teams can expect to face across the US. I think the challenge within this situation is more for teams playing each other outside of their conference rather than in it. This is due because the months of the season may spring some surprises on teams. If you watched the first YouTube video above (under the MLS header) then you should easily understand this next part.

For instance, let’s take LA Galaxy (Western Conference) and New York City FC (NYFC) (Eastern Conference) this season. LA are due to play in Houston twice this season while NYFC are only visiting once. The same statement can be applied to Houston travelling to LA and NYFC or any other team for that matter.

As an example, a team coming to Houston in March/April will most likely experience very different weather conditions than if they play in July/August. The summer temperature can reach up to 37 degrees Celsius and average around 28 degrees Celsius (although the humidity always makes it feel hotter). With this humidity, come thunderstorms and heavy rainfall (which delayed our home game vs Toronto by 24 hours in August). Other examples can be found in March/April when playing Vancouver away (possible freezing temperatures) and all year round in Colorado having to deal with altitude.

Travel & Turnover of games

 If the US was not as big as it was and only had to deal with the weather conditions that it has, playing there would not be as difficult. The travelling factor can take it out of anybody and everybody. The hours spent between home and the airport, the waiting around to depart and at baggage reclaim, the journey to the hotel and if needs be accustoming to the new time zone can drain even the best of us. The US is so big that travel can take a while (6+ hours between New York and Los Angeles) and even longer if you are delayed by the extremities of the weather.

I could now go off on a tangent and explain this through examples yet why not read about it straight from the bookshelf. This past week, there was a big discussion about travel in MLS. Most notably why MLS is so behind the times when compared to the other professional sports leagues in the US. The LA Times wrote about travel plans & schedules in MLS while Brendan Kent of American Soccer Analysis (ASA) investigated the same topic from a statistical standpoint. These are not the only links available and certainly won’t be the last ones.

Short detour:

Now that I have mentioned ASA, they are a great bunch of guys with some real knowledge of the game and should be used more often by MLS teams. I’m sure they would be thrilled to help your club find the next cheaper version of a Giovinco, Villa or Wright-Phillips.

screenshot-145Back to travel. Without a doubt, all this travelling can certainly take it out of your players and your team. How does the manager take care of his players in this regard? Thankfully, that was not part of my job description (we as analysts already have enough to do) yet it would still interest me. How might it look from an analyst’s perspective? Oliver might be able to answer that if you ask him nicely.


The implementation of a system such as the salary cap is a common tradition in American sports. This allows the league to primarily thrive on a more balanced level as can be seen by the different winners (10 teams) of the Supporter’s Shield in the league’s 20 year history. The league has undoubtedly received criticism about the legitimacy of using such a system by Consultancy Firm Soccernomics.

MLS is also the first league to my knowledge (feel free to correct me if I am wrong) to publicly release each player’s salary details and this has caused quite a stir as such.

Elsewhere, maybe not directly related under this heading but still worth a mention is assembling an MLS squad based on a salary cap. Opta who collect match statistics in a number of sports, recently partnered with the University of Columbia and Havard in allowing students to build the optimal roster. Clicking here shows the methodology one team used.

Lastly, if you fancy reading through the pages and pages of MLS Roster rules, please click here.

A Day in the Life of a Performance Analyst

So what does a working day really look like? It varies a bit from day to day as it’s not your standard Monday – Friday 9am – 5pm job. The hours vary depending on the upcoming week and the reasons could be due to 1) When is training scheduled, 2) How many games are this week and 3) It’s Game Day!

For the most part, training took place in the mornings (09:30 – 11am) or occasionally in the evenings at 5/6pm. For the morning training sessions, the days started whenever the alarm clock rang (for me it was 06:30am). As long as you were in work for before 8am, it was a ok. When we trained in the evening, the coaching staff usually got to the office at lunchtime and stayed until 9:30pm. Also if you think the work is done during these “office hours”, you are greatly mistaken. Often you will need to complete another bit from home, more or less to the annoyance of your other half!

Nevertheless, below is an example of how a typical day (let’s use Monday) panned out for me in Houston.

  • Wake up 6:30am
  • Quick breakfast at home
  • Get to the training ground for 07:50am
  • Check updates on Twitter (the football analytics community does some really cool stuff), quick read of the mornings news (Brexit and so forth) and reply to some emails. This would usually take about 20 – 30 minutes.
  • After that I would start managing the data and video on our next opponent and start with the pre-match report.
  • After working on the pre-match report for the last hour, training would have just started (09:30am). I grab a bite of “second breakfast” (if the players leave anything) along with water bottles and head out onto the roof.
  • 11:00am – Training finishes and I start to breakdown and format the video for the coaching staff. Quick shower and then its lunch time.
  • After lunch, I continued with the opposition report unless other tasks were asked of me. Occasionally, the goalkeeper coach wanted to discuss evaluating goalkeepers through data so I jumped on that with reports.
  • In cooperation with Oliver and the academy staff, I continued building a form of feedback report for the academy to use.

What does game day feel like?

For the first time being on the other side of the fence, I can honestly say (to me) it doesn’t feel very different to being a fan. I never travelled to away games so I cannot share my experience with you on that but I was present for home games. Sure you are there to work but you are also one of the privileged few to work in the sport you love (although the hours and weekends are taken away from you). Yes we work while the game is on, but deep down, I am still a football fan, excited to see the team play well.

Standing next to the TV cameras where we filmed the games, there were times just before kick-off during my first couple of games where I had doubts regarding the opposition – “How much did we really know?”, for example, maybe they changed the central midfielder and now he’s playing on the wing. The question of ‘have I done enough to prepare the team’ went through my mind. I quickly realised that these were really rhetorical as it came down to the players executing the game plan we developed.

It’s impossible to know and predict of how the opposition might play but we do our best (along with the coaching staff) to prepare the players for what we think is going to happen.

*Reminder to view the screenshot image I took from Colin Trainor above. *

By now, I think each team has their way of managing the extremes in MLS, except maybe per se for newcomers Atlanta & Minnesota United. Overall, all the above factors that were discussed give fans and aspiring analysts an example of what MLS is all about. Clubs will always know who they’ll play in their own conference but outside of their conference is anybody’s guess. (That’s of course until the fixture lists gets released). The only thing that most likely is not going to change is the conference you are assigned to (as this is based primarily on the cities location).

That is all from myself regarding my stay in MLS. I do blog from time to time either for American Soccer Analysis or via my own blog. Currently, I am gathering team and player statistics on the German Bundesliga via my Tableau Public profile. I also plan to write some more in-depth analysis on the Bundesliga soon, so watch out for this space.


Alex Rathke


Explaining and examining per 90

There a number of excellent sites that gather football statistics such as Squawka, Whoscored, and Transfermarkt. From these sites, we can collect enormous amounts of data about players. This could vary between shooting data, passing data or even off-ball movements of players (to name a few).  Let’s examine the shooting detail in more detail. First, we can measure the number of shots any certain player took. Following this, we can break the shots down further into shots on target (SoT) and goals scored. While this only explains to us the quantity of shots taken, we can also measure the quality of them through xG models.

Once we established the quantity and quality of shots taken by a player, we should aggregate these as benchmarks. Another way of describing this is to use the term called p90. This term allows us to compare players against each other to see who is a better shooter (by their minutes played) on a game by game or even season by season basis. The talented individuals writing for Statsbomb, in this case Benjamin Pugsley wrote a great article a couple of years back explaining per90. If you are not familiar with p90, then please read this article before continuing below.

At the end of the 2015/16 Premier League (PL) season, I wrote about evaluating my xG model with data that I collected from the PL. At the end of the post, I included a picture of all players’ goals vs xG scored. However, as Pugsley mentioned, “there is a problem with these raw, basic numbers”.  This problem being that these numbers are not exactly accurate. So below, I adjusted each players’ shots, SoT and goals scored to their p90 minutes (with the help of and. Let’s have a look at the graph.

Unfortunately, I do not think the graphs tell us a lot, well except for the relationship between a players’ Shots p90 and SoT p90 (R2: 0.55, P-value: <0.0001); Shots p90 and Goals scored p90 (R2: 0.31, P-value: <0.0001) and lastly SoT p90 and Goals scored p90 (R2: 0.32, P-value: <0.0001).

There is one element though within all of this that I do find surprising. Between “Shots p90 and Goals scored p90 (R2: 0.31, P-value: <0.0001) and lastly SoT p90 and Goals scored p90 (R2: 0.32, P-value: <0.0001)”, the R2 value only increased by 0.01. Based on these results, SoT are only a very slight (0.01) improvement in better predicting goals scored than shots taken. Logically, we know that this is not true as SoT are closer to being converted than Shots taken. Lastly, as the R2 value is low, I am certain that there are other/better factors that could have a stronger relationship than those described above.

2015/16 Top 20 p90 PL goalscorers in the PL

As each players p90 statistics have already been calculated above, I now just need to filter the players by minutes played. I decided that players needed to have played at least 750 minutes to be eligible for this next part. In the table below, I have sorted players by p90 goals scored for the 2015/16 season.


*I will provide the data here, so you can filter the list as you wish*.

I also made the table more visual and appealing to the eye through Tableau. The table is quite detailed yet for the purposes of this blog post, I am only interested in p90 statistics than the players’ xG figures. The graph is divided into three parts: 1) Premier League Shots p90, 2) Premier League SoT p90, 3) Premier League Goals p90.


After examining the graph in more detail, let’s take a closer look at the numbers, what they mean and how to read them. For example, Tottenham striker Harry Kane took on average 4.27 Shots p90 (3rd); 2.11 were on Target p90 (1st) and he scored 0.69 goals p90 (4th). Kane had another fantastic season with Spurs and he has been a very consistent player for them.

Now that we know how many shots each player took, what about the quality of the shots they took? To examine this, I have broken the pitch into 8 zones (picture below) and distributed the shots (taken by each of the 20 players) among these zones. Of course, it would be so much easier to map the shots using XY coordinates such as the one here. Unfortunately, I do not have access to this data and if I was to collect XY coordinates of all shots, it would take a very long time.

Not to worry though as I have been able to distribute the shots to marked zones (as mentioned above). The next graph may look a bit messy at first, yet I find the data is summed up very nicely.

Again, let’s examine the data on Harry Kane as an example. As the picture shows, the central area in front of goal is marked as Zones 1 & 3. From these zones, Harry Kane’s record is as follows: (Zone 1 – Shots: 6; Goals: 3; Accuracy: 50%) and (Zone 3 – Shots: 27; Goals: 11; Accuracy: 41%). While the sample size is small, these numbers, in general, are not surprising. This is due to what has already been discussed numerous times in football analytics and what also makes logical sense – the further away from the goal you are, the less of a chance you will score. Also note the number of shots taken and goals scored from more of an angle (Zones 2, 4, 5 & 7).

I hope you enjoyed this post and I look forward to any feedback/questions that you may have.

Merson’s league table 2015/2016

The Sky Sports pundit Paul Merson on a weekly basis predicts the scores for the upcoming weekend’s/weekday Barclays Premier League matches. I must admit he is a very outspoken character at times and does have some odd or even bizarre views along with the rest of the Sky Sports’ Soccer Saturday crew. On the 4th September 2015 (around 3 and a half weeks into the 2015/2016 campaign), Merson revealed his end of season league table prediction. Using the half-way stage of the 15/16 season, I will try to explain and evaluate his choices. First however, let’s have a look at some of his previous predictions.

The popular football highlights website with the help of Twitter users “Simply Spurs” after 13 games plotted the Merson’s league table (as they called it).


Now let’s have a look at Merson’s league table for the 2015/2016 season and compare it to how the league table stands half-way through the season now.

At first, I would not necessarily see anything wrong with Merson’s predictions, however in more depth I would question his positioning on especially Newcastle, West Ham and Tottenham. As we all remember Newcastle were in a desperate situation towards the end of last season when John Carver became coach. While he regarded himself as “the best coach in the Premier League”, his tactics and style of play (4 pts from the last 11 games) did not bring any flair of excitement to the dear Newcastle supporters. They just avoided relegation on the last day due to Hull City losing to Manchester United and therefore they dropped into The Championship.

West Ham had a phenominal start to last season’s campaign (17 pts from 10 games & 31 pts after 19 games) yet Sam Allardyce’s tactics were not well received around Upton Park. With the move to the Olympic Stadium looming it is understandable that the Hammer’s board wanted a strategic change. Former Hammer’s player Slaven Bilic was brought in to replace Sam Allardyce and brought with him the tactics that West Ham fans wanted to see (20 pts from 10 games & 29 pts after 19 games).

I feel that Tottenham have finally found a manager that can bring them sustainable success. However what does that mean at Tottenham, especially when Daniel Levy is Chairperson? Levy has had a reputation in “needing” Champions League success regardless of how many managers it might take to get there. We all remember the transfer saga when Gareth Bale was sold for a world record fee to Real Madrid and Tottenham went to a spending spree. The amount of players and quality that Spurs brought in was astonishing and truly below par. Is it possible that Potchetino’s watch is ticking at White Hart Lane if they again fail to qualify for the Champions League?

Understandably Merson

I can understand Merson’s predictions about Watford being relegated straight away back to the Championship. In Watford’s previous two Premier league seasons’ (99/00 pts 24 & 06/07 pts 28) they were relegated both times. They already have 4 points more since when they were last relegated, yet how much does that mean in terms of survival? For me, the main threat which will help them survive and flourish further on into the season is the strikers’ partnership between captain Troy Deeney and Nigerian International Odion Ighalo.

Surprise Team of the Season

Without doubt though, Leicester City are the team that have ‘positively’ surprised everyone this season. When Nigel Pearson got replaced by former Chelsea boss Claudio Ranieri early into the season, we were all shaking our heads some bit (yes, even you. Just admit it). His unique style of play has allowed Leicester to play counter-attacking football and with players like Jamie Vardy, Riyad Mahrez, and N’golo Kante they have the pace and space to break and expose opponents.

Obviously there are other teams that have sprung a surprise on us and will continue to do so throughout the season. One of these teams will undoubtedly be Crystal Palace and Watford.

On the other end of the spectrum, Chelsea were the surprise ‘negative’ team of the season. The first matchday saga between Mourinho and his medical staff is what seemed to cause some internal political chaos in the Chelsea dressing room. Before Mourinho got sacked in early December, it seemed that he tried everything to get his players going. He used different approaches such as critising the players, protecting them and blaming the referee. None of the methods seemed to work and we was dismissed. Could it simply come down to the 3 season effect that Mourinho brings with him? How will he ever manage a national team?

Top 4 Picks

I have a strange feeling that the Top 4 (Leicester City, Arsenal, Manchester City and Tottenham Hotspurs) will pretty much stay like it is now (although not necessarily in that order). By May, the title fight will be between Arsenal and Manchester City while Leicester City and Tottenham will fight it out for third and fourth place respectively.

The relegation contingency

Believe it or not but according to my calculations every team below Manchester United (after 19 games) had a chance of being relegated this season. Obviously this percentage number is small where United stood but increased as you went down the table. Now a couple of games onwards (23 games played), every team below Southampton are at risk of being relegated.

My crying three

Sunderland and Aston Villa have both been playing with relegation the last couple of seasons. In particular, Sunderland have been close on a number of occasions (2012/13, 2013/14 & 2014/15). Yet, Aston Villa have not been much better (2011/12, 2013/14 & 2014/15). Even with all of Newcastle’s new January signings, they may be joining Sunderland in playing the North-East derby in the Championship next season.

18. Newcastle    19. Sunderland   20. Aston Villa

If Aston Villa, Sunderland & Newcastle were to drop down, then it would be the first time since the 2011/12 season that the three promoted teams in this case (Bournemouth, Watford & Norwich) were to stay up.

Enough writing for once and now go off and enjoy the FA Cup games this weekend.

Thank you for reading this and please tell me your predictions.