Tag Archives: Analysis

TPOEM 3pm Predictions

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Judging by the above, TPOEM may well be overestimating the probability of draws in general. The difference to Skybet odds suggests below par value in betting on Arsenal, Villa, Everton, Liverpool or Southampton (although might be slightly better with other bookies e.g. Betfair).

Staying true to the value-principles of the model, I’ll back draws for for Reading-Liverpool and Arsenal-Norwich even though neither result represents the most probable outcome.

In the next week or 2 I’ll post some more information on how TPOEM works, why I’m doing it and what I use it for. I actually use the model primarily for player appraisal, judging ex-post player performance (i.e. past results) rather than just to try and beat the bookies. Nevertheless, the prediction side is an interesting and amusing exercise (at least for me anyway!) – however this is still in its infancy and I am tweaking it every week at the moment.

The basics of TPOEM have striking similarities to Neil Charles’ model, which you can follow here: http://www.wallpaperingfog.co.uk/2013/04/football-model-under-hood.html

I also use Excel and VBA (more than I ought to, it certainly slows me down) and EPL Index player data to arrive at game expectations, but we are coming out with very different results – that just goes to show how important the modellers’ input weightings and choice of variables are to any model.

Martin Eastwood’s EI index is another worth checking out, although on the face of it it does appear to be more of a top-down approach to predicting: http://pena.lt/y/2013/04/12/ei-match-predictions-for-the-english-premier-league-6/

Top-down weighted models (focusing on macro- rather than micro-level data) are likely to be used more heavily by bookmakers which suggests that (assuming I’m right about the construction of the EI Index) Eastwood’s model may have a better chance of successfully predicting results than either TPOEM or Charles’ model. But unless any one of the models is spectacularly bad, we’ll need a lot more information before that becomes clear. Healthy competition in any case!

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The Science + Football Conference: Day 2

Last up in this series of reviews covers the second part of the Science + Football conference.

On day 2 of the conference I kept a low profile and stuck to my seat in the presentation theatre for most of the day. It was another day of sessions from a wide variety of speakers (as by now I had become accustomed to) including psychologists, sports scientists, statisticians, scouts and a panel session including former England manager Steve McClaren.

Dr Misia Gervis, who I noted in my earlier review of the Sports Analytics Innovation Summit, gave a presentation which really struck a chord with the post I wrote on Saturday evening. A senior lecturer in sports psychology at Brunel University, Gervis’s talk discussed positive psychology and how it can be applied at football clubs. She is actively involved in efforts to bring psychology into football clubs so that it can be used to benefit players and performance. Actually, in a follow-up to my earlier post, I had already been advised to look into the work of Jacques Crevoisier whose work with the development of psychometric tests for Liverpool and Arsenal has been well-documented (although I didn’t know of him before this tip). Gervis discussed resilience: “the ability to take hard knocks, to weather the storm and to value oneself no matter what happens” – this is affected by fear of failure, perfectionism, injury and criticism with a further impact on emotional control and decision-making. She highlighted the importance of using the concept of ‘signature strengths’ with players, where their best attributes are identified and developed to help create the right conditions for them to flourish.

We were also treated to a couple of lectures about fitness planning and training regimes by Dr Peter Krustrup of the University of Exeter and Matthew Cook, head of sports science for the MCFC academy. Both discussed how optimal fitness training for footballers involves training sessions which mimic the movements and levels of activity in a match. Krustrup included work from one of his studies, showing how yo-yo training (high intensity intermittent exercise) performance was a better indicator of match fitness than VO2 max testing – although there is a correlation for footballers. Cook explained that for academy prospects at Man City, they go so far as to look at the biological age of players vs maturation levels to try to ensure that developing players are not discriminated against in comparison to faster-growing players.

That last point links in nicely with Blake Wooster’s presentation. Wooster, business development director at Prozone, described his role as a kind of coaching scientist. His views represented the future of analysis in sport when he said that clubs should “use analytics to drive and not just inform decision-making”. Wooster’s session tied in with Rasmus Ankersen’s presentation from the day before (and to a lesser degree Cook’s reference to youth maturation) as he discussed the relative age effect in youth team football. He showed how different youth age groups are concentrated towards players born in the months directly following the cut-off point because the oldest boys are likely to be the most developed e.g. where the cut-off is 31 December, players selected in a football team are most likely to be born in January and February. He went on to describe the current Belgium national team, which has an incredibly strong first 11 at the moment, and how in recent years they overhauled their age groups to include 2 separate teams – one ‘A’ team and a development team called “the futures”. Wooster also gave an example of how Prozone calculate expected pass-success rates vs actual success rates to analyse youth players and potentially identify undervalued talent – this for me was a very satisfying use of stats to aid player appraisal. Wooster, however, did admit in the later panel session that analysis is still in an embryonic stage and that the term ‘moneyball’ in football is not particularly useful in selling analytics to clubs.

For what it’s worth, the presentation that I thought was the most interesting and well-measured throughout all of the 30-odd sessions I saw over the 4 day period was from Liverpool’s Director of Research Dr Ian Graham. Graham joined the club in the summer of 2012, following 7 years with a football analysis company. His presentation, entitled “The trouble with statistics” included the right balance of caution, care and logical proofs in answering a simple question: are clean sheets more important than scoring goals?

Graham’s regression analysis showed that one extra goal scored for a team is worth 1.02pts on average, whereas one extra clean sheet is worth an additional 2.99pts. From that piece of information alone I suppose one could be forgiven (if you want to be kind) for thinking that clean sheets are indeed more important than goals scored. But the R-squared of goals scored vs points is 77% whereas for clean sheets vs points it is 65%. The relationship with clean sheets is weaker because of volume – clean sheets are a limited resource whereas goals scored are unlimited in a match. In order to improve from an average level of goals scored (50 per season) to the top quartile you would need to score about 10 more on average (+20%). However in order to go from 11 clean sheets (average) to the top quartile level of 14 per season you need to improve by +27%.  Hence we might say it is ‘easier’ to score more goals than to improve clean sheets. Having shown this, Graham explained that the FA really was a pioneer in football in 1980 when it became the first association to introduce 3pts for a win in order to incentivise attacking football (not that it had a major long-term effect). He also discussed the path of strategies for teams at different levels – showing that clean sheets are still relatively more important for below average sides who are less likely to outscore a top team and will have a better chance of success if they restrict their opponents from scoring.

The last session I attended was the coaching panel with Steve McClaren, Paul Holder (FA national coach) and Scott Miller (first-team fitness coach at Fulham). As I noted in my previous post, McClaren began by talking about how great it was to be able to support an exhibition of innovation in football, before saying that “all I want from you science people is fitness and injury stats”! He insisted that management needs be allowed to work on instinct – which just goes to show the reality of the challenge that analytics has to overcome if it will ever become fully integrated at football clubs. He gave some useful insight into his knowledge of the differences in coaching between the Netherlands and England – in the Netherlands it seems that football-related training and fitness training with a ball are given more of an emphasis. McClaren used his experiences from Twente and Wolfsburg to argue that game intelligence in England needs to improve, giving an example of a young player at Twente who, when he was asked his opinion on team tactics for the upcoming game, gave a such a full account of player positioning and where to concentrate attack/defence with a good enough understanding to be one of the coaches.

One of McClaren’s final points was that “coaches go into a comfort zone where they don’t seek to learn more. More coaches should get out of their comfort zone and try to learn new skills and gain knowledge and experience”. Finally a positive from him that could be taken for analytics, although unfortunately he wasn’t talking about the use of performance data by coaches!

Final note

I have been quite prolific over the past 7 days in terms of writing and reviewing the conferences and this is for 2 main reasons. Firstly, I have been inspired with ideas and enthusiasm after attending the conferences – for anyone with a serious interest in sports analysis I would definitely recommend getting a ticket for either (or both) next year. Secondly, the readership of my blog has increased well beyond usual levels since I started it about 6 months ago so thank you to everyone who has taken an interest and in particular retweeted/shared the link of my blog to followers, colleagues and friends. My enthusiasm in posting the reviews quickly has meant it has all been a little unfiltered but I have tried my best to keep them as informative as possible!

 

Model pitfalls and further discussion of TPOEM

Since my previous post introducing a new model for football analysis, TPOEM, I have developed and integrated some significant improvements to it.

Firstly the speed in which I can give predictions based on team starting line-up (involving less manual input, more automation) is much better, so last Saturday I was able to tweet about the model’s predictions well before the 3pm kick-offs began.

Secondly I have added a manager/leadership factor into the analysis which is dynamic and unique to each team.  This adjustment is intended to ‘smooth’ the team level aggregate scores that TPOEM calculates, where the model would not otherwise capture a persistent difference between a team’s results and their underying scores. This offsets (albeit not completely) the difference between the model’s league table compared to the actual league table. Why does that happen? Well, the basic underlying reason is the same as why a shots on goal league table does not reflect the real league table. I attribute this to a kind of quality factor that I am not picking up in the statistics I use: quality in terms of shooting can relate to the position on the pitch of a shot, whether defenders pressured the attacker and how much of a contribution the assist added to a goal scored. This quality factor will also incorporate a team’s record at home or away. For reference, the model currently seems to think that Stoke and Norwich are outperforming particularly well whilst Wigan, Southampton and QPR are all doing worse in the league than TPOEM suggests they should be doing. That might be due to luck, team playing style, management, player leadership, quality or all of the above. The model should now be slightly better at accounting for that.

Predicting part 2

So the first week of predicting using TPOEM brought me a net proft, although my biggest win was West Ham away win vs Stoke – and I’ve already explained that the model was distinctly anti-Stoke before the most recent update!

Again, as ever, I am seeking value so even if TPOEM suggests a probability of an event win/draw/loss of about 40%, if the bookmakers quote odds of 35% then I consider it an attractive bet. As it stands I haven’t been that selective about what I bet on: in fact so far I’ve been betting on every match that I ran the model for even though in many cases the model didn’t really suggest any particular value vs bookies.

The result this week, from 5 games, was another net profit, this time +26% return (it was +56% last time). But that came from 2 wins, 1 void, 2 lost bets, so in a sense the net result was neutral.  I profited overall because I weighted my bets towards the most attractive in terms of value – the biggest win being a draw-no-bet backing Everton at home to Man City. The model really liked Everton’s chances mostly because Kompany, Aguero and Yaya Touré were all missing for Man City.

I also backed draw-no-bets for Liverpool, Villa and Stoke: lost, won, void respectively. And lastly I went with a draw for Swansea-Arsenal (lost) but in retrospect I shouldn’t have bothered with that bet because the model gave no conclusive direction for the game and the odds weren’t good either.

As I reformat the model’s data and find a better way of communicating its predictions/results I will publish more information on the blog as I recognise I have kept most of the details pretty close to home so far. When I’m at my desk for the 3pm kick-offs I will also tweet about the model’s predictions so if you’re interested look out for that but if you bet then you are doing so at your own risk!!!

Introducing TPOEM

I must say I sometimes get irritated by the overuse of acronyms in today’s world but this time I’ve created my own. TPOEM rather unimaginitively stands for The Power Of Eleven Model which I have been developing over the past few weeks.

TPOEM is the culmination of fairly light research into simple OPTA-derived football statistics that I have been analysing over the past 6 months or so. Having only really put the information together over the past week or so, it is a bit foolhardy to discuss TPOEM in any detail right now – but I have already begun using it to objectively rate player/team performance and even test its efficacy at predicting match results.

I will give some detail into how the model works. The first point of note is that it is a bottom-up system.  That means that it primarily analyses player data first and team data second. There are many reasons I wanted to approach the analysis in this way:

  • A focus on player statistics gives an objective view of a player’s importance to a team, and can help indicate which players contributed most/least to a team’s performance
  • Player statistics like goals scored and assists are readily available and easily compared between players at different clubs
  • TPOEM can potentially capture information that is useful to understanding team playing styles
  • TPOEM can potentially be used to give a prediction of a match result based on the team starting line-ups, which will give a clearer expectation of a result if key players from either team are missing

Although TPOEM is derived from fairly simple statistics, the most recent iteration incorporates 36 statistics including stats from goals scored and shots on target to tackles and ground duels. I have weighted the utility of each action and applied success rates where available to give a rating in simplified categories:

  • Defending/Ball winning
  • Passing/Ball retention
  • Attacking
  • Discipline
  • Involvement
  • Goalkeeping

Of course the overall scores are adjusted so that the most frequent actions (passing, touches, etc) do not grossly outweigh the less frequent, but arguably more important, actions such as shots on target and goals scored. At the same time, I tried to maintain some care over the relevance of goals as a statistic – of course goals win games, but why should TPOEM rate attackers more highly than defenders because they score more often? Strikers often take all the plaudits for scoring goals but since most goals are scored inside the box I have tried not to unduly credit a goal scored – in many instances it is easier to score a goal than miss. I took a similar view of assists, seeking not to overly ramp-up a player’s score simply because he completed a pass (however important it was). I have to stress that it still wasn’t quite a finger in the air approach to rating – I have reviewed correlations to team performance at various layers with the aim of giving my weightings a scientific basis.

I have now tinkered with the algorithms enough times to realise that although TPOEM in one sense gives an objective rating of player performance, but in another sense remains a reflection of its creator’s biases and research. This is limitation of any model, which can only be improved by testing and further research.

What about results? Well I will keep publishing information over the coming weeks as I look to find suitable ways of presenting TPOEM’s output.

For now, I have run the model on the first 271 games of the premier league season (i.e. before the kick-offs on the 2 March), and I can announce its candidates for the most man of the match performances so far this season:

Player MoM awards
Santiago Cazorla 13
Gareth Bale 10
Adel Taarabt 8
Eden Hazard 8
Leighton Baines 7
Luis Suárez 7
David Silva 6
Dimitar Berbatov 6
Juan Mata 6
Marouane Fellaini 6

This highlights the importance, according to TPOEM, of Santiago Cazorla to Arsenal’s season in terms of match-winning performances. Both Manchester sides and Arsenal lead the team man of the match awards with 22 apiece, the difference being that there is a much larger spread of players who have put in top performances for United and City in the league.

Predicting

Those readers who follow me on twitter will have noticed that TPOEM liked the value of the chances of a home win for Everton and draws for Swansea vs Newcastle and Manchester United vs Norwich. Please note that this isn’t a direct match result prediction for the above – TPOEM actually had all 3 as odds-on for home wins, but the probability of a draw when compared to quoted bookmakers odds before 3pm seemed attractive at the time.

The main problem I had was in finding an efficient way to input all the line-ups in time for kick-off!

As it was, I completed my efforts and placed bets on all the 3pm kick-offs by 3.25pm – something I will have to work on going forward.

In addition to the above bets, of which only Everton’s home win against Reading paid off, I bet on a draw for Sunderland-Fulham (profit) an away win for West Ham (profit) and a win for QPR. 2 of these bets were actually placed live, with the scores at 0-0, whilst QPR were already 1-0 up at Southampton when I took the gamble of backing them to win. According to TPOEM, Chelsea were massive favourites at home to West Brom so I decided not to bother with a gamble on that game.

Most pleasing was the away win of West Ham at Stoke – a game which I am sure could just as easily have gone either way. When I ran the line-ups through TPOEM West Ham had actually already made 2 early substutions so I incorporated those new players into the line-up. The model indicated about a 30% chance of West Ham winning which was attractive enough when compared to quoted odds of about 9/4. Fortunately for the early prospects of TPOEM they duly achieved an unlikely result at the Brittania.

I will continue to test TPOEM’s predictive efficacy vs bookmaker odds but for any followers of the blog, please note that I am seeking value not outright wins. Even if Manchester United are heavy favourites to win at home, as they were at the weekend, I may suggest another outcome if the odds are attractive enough depending on what my early-stage model tells me!

Defence Against Dribbling: 2012-13

This is a follow-on from my previous post on the top dribbling teams and players this season in the Premier League.  Since I took the time to prepare the data to review the best dribblers with the ball at their feet I thought why not flip the information to review the opposition as well?

This is not a completely straightforward exercise because the information I have available does not identify the opposing player(s) involved when a dribble is attempted – and even if a defender does not actively tackle his opponent, his position may force the attacker into losing the ball (eg. by running out of play or into another defender). I can’t provide much insight on these ‘micro’ events on the field.

But, we can do a similar team analysis as produced in the last post to consider which teams seem to invite dribbling against them, and also which teams are particularly adept at making opposition dribblers lose the ball.

Dribbling Against FrequencyThe above data is ordered by total dribbles against. We can immediately see that teams tend to dribble more often against Sunderland and Norwich (382 and 380 attempted dribbles against respectively) and least frequently against Everton (267 dribbles against). With all teams having played 23 games at the time of writing, this difference of about 5 dribbles per game against perhaps isn’t terribly telling but may give an indication of team tactics without the ball. QPR and Reading, both in the relegation zone, sit at opposite ends of this table.  Alternatively, Everton and Spurs are on the low end, allowing the fewest number of dribbles against, whilst Arsenal are 4th highest despite their league positions of 5th, 4th and 6th respectively. I’m intrigued by this, and admittedly I haven’t given it a lot of thought before just typing away now, but at a guess team pressing plays a part here – Moyes’ Everton in particular have a reputation for pressing across the pitch whilst AVB’s Spurs are beginning to develop a reputation for pressing high up the field. Maybe that has a bearing?

Of course, there is more to defending than pressing or allowing attackers space to dribble. Indeed, even with tight marking some players will seek to dribble to gain a yard against his opponent on the turn.

Here’s a bar graph comparing total dribbles against (as above) but now vs total shots allowed:

Dribbling Shots Against Frequency

 

I’m not particularly fond of information for the sake of it and I’ll admit that the above graph is a bit of a jumble, but we do get a clearer picture of Arsenal and Reading in particular who allow disproportionately fewer (and more, respectively) shots against when compared to their dribbling against stats – perhaps adding some depth to our understanding of how effective their defensive style has been.

What about the success rates of opponents dribbling against?

Dribbles Against - Success RatesThe above graph tells us how successful the opposition has been at dribbling against the team in question. So, although Everton allow the fewest number of total dribbles against, they have the second highest success rate against – i.e. 53% of dribbles against them are successful. Wigan seem to put up the least fight when it comes to dribbling against with opponents having 54% success on average. But most teams are spread a few points either side of 50%, with a few outliers: Manchester City, Spurs and Southampton – all of whom are no pushovers when it comes to dribbling against. City in particular have an exceptional 37% (now I’m starting to wonder why I didn’t turn the success rate on its head for intuitive purposes!). To translate that, around 1 in 3 attempted dribbles against City are successful, which is the lowest rate in the league this year and highlights their strength across the whole pitch.

Dribbles Against Per GameThis last chart is ordered by the number of successful dribbles against per game, so Everton move up a few places and Manchester City take up the position on the far right – allowing on average just under 5 successful dribbles against per game. Manchester City incidentally have conceded the fewest goals so far this year.

What can we learn from this? Obviously there is more to a game of football than how often you allow the opposition to dribble successfully against you but this is still an interesting objective view of the differing defensive styles/abilities of teams in the Premier League and can no doubt be used in tandem with analysis of other defensive measures to improve our understanding of what tactics have been most effective.

 

 

 

Dribbling wizardry: 2012-13 so far

Dribbling is a much-admired skill in football. The best dribblers in the game are fixed into the history of the sport: players like Stanley Matthews, George Best and Diego Maradona are remembered for their almost superhuman ability to leave defenders trailing in their wake. Beating defenders 1-on-1 excites a crowd in a way only shots and goals can match, but in the same way that a poor effort on goal from a good position can frustrate supporters, losing the ball when dribbling in a good position can be just as frustrating!

In this post I look at some of the best and worst dribblers this season in the Premier League: heroes and villains.

Teams Dribbling frequency

So far this season there has been little correlation between the number of times a team dribbles and whether or not a team gets results. As we can see from the above chart, the top four in terms of total dribbles attempted include Liverpool, Newcastle and QPR whilst league leaders Man Utd are mid-table in terms of dribble frequency.

Liverpool fans are treated to a lot of dribbling at Anfield, but as we can see from their success rates the fans are frustrated more often than not: on average they lose the ball through dribbling about 12 times per game.  Although Arsenal attempt about 1 less dribble per game than Liverpool their average success rate is better: Arsenal have the best mean dribble success per game of c.11 successful dribbles vs 10 unsuccessful. Brendan Rodgers and Arsene Wenger clearly to not mind letting their players dribble more freely than Sam Allardyce. West Ham’s dribble success rate is actually slightly better than Liverpool but they attempt less the half as many dribbles per game.

Dribbling success rateTeam dribbles per game

Some of this is perhaps not neccessarily due to team tactics but the amount of time spent in the opposition’s half. Of course, when in the defending half, players generally know better than to take risks with the ball at their feet – so we would expect dribbling to occur mostly in attacking positions by attacking players. We must bear in mind that the dribble statistic used here gives a simple success/fail result per dribble without any additional information on the difficulty of the dribble, the position on the pitch the dribble was attempted or the actions directly after the attempted dribble.

Aston Villa have the best overall success rate in dribbling (62%) but they have only attempted one more dribble than West Ham – this suggests that Villa players are very selective in choosing when to dribble or at least leave the dribbling to their most skilled players.

On the other end of the scale, Stoke and Swansea have particularly bad success rates when it comes to dribbling: 36% and 37% respectively, meaning that only about 1 in 3 dribbles are successful.

Players

For the tables/data below I filtered out any players who had played fewer than 500mins and attempted fewer than 15 dribbles.

Most frequent dribblers

From the most frequent dribblers table above we start to see the driving forces behind the team stats published earlier. Ben Arfa is arguably the dribbling king of the premier league as he attempts almost 9 dribbles per 90mins and yet still manages a positive success rate of 58% (although as a Newcastle fan perhaps I am biased!). Opposition players know Ben Arfa is likely to dribble when he is on the ball and yet they still don’t usually get the better of him. Suarez and Sterling are clearly the main contributors to Liverpool’s high frequency as they account for more than half of all Liverpool’s attempted dribbles but they both have a below average success rate. This is certainly offset by their high rates of chances created from open play. The surprise success story of the table is Samba Diakité who has a strong 66% success rate despite having a high propensity to dribble.

Most successful dribblersThe most successful dribbler is another Newcastle player: Cheik Tioté, who has a ridiculous 84% success rate. In fact this table is an interesting mix of defensive and attacking players. Diakité again stands out, but also Dembélé and Cazorla who have both attempted more than 60 dribbles and still have an superb rate of success.

Dribblers - don't bother

Last but not least, the above table shows the very worst dribblers this year. Stand up David Luiz. To be fair, 8 of the players in this list of 20 have attempted fewer than 20 dribbles – but David Luiz would certainly be better suited to playing it simple as he has only been successful with 2 of his 15 attempts.

Kightly, Dyer and Kacaniklic are arguably even more frustrating for their managers and fans since they seem to be a little over-confident in their dribbling ability! They each succeed in around 1 in 4/1 in 5 of their attempts to beat a defender. PASS THE BALL!!

Different views of the League Table

In this post I have published a few simple Premier League team stats with consideration to the insight they provide compared to the current league table.  This can be an interesting exercise to review certain measures and analyse their efficacy in explaining team performance. These types of stats should be taken with a decent pinch of salt, because at this stage in the season (and even after 38 games) the statistics are dynamic and the sample size is small – team averages are constantly evolving and prone to ‘anomalous’ results, such as the 8-0 win by Chelsea against Aston Villa which has had a significant effect on goal difference. For the purposes of this post, I have made no attempt to smooth or adjust uncommon results.

Total Shots

Let’s start off simply by looking at total shots. This includes shots on target, off target, speculative long range efforts, blocked shots and even scuffed shots that go out for a throw in.

Total Shots TableI have included how many home and away games each team has played because it can matter to the mean values for this sample size. For example Arsenal have played the least home games with 9; at home they average 17.3 shots per game whilst away from home they manage 13.4, a difference of about 4 shots.

As we can see from the table, Liverpool are currently the undisputed shot taking champions with 1.4 shots per game more than second placed Tottenham. Stoke on the other hand lie bottom and take on average only 9.8 shots per game.

The biggest outliers when compared to the league table are Stoke and QPR, whose positions are reversed. We can see quite clearly from this strange result that total shots is not a good indicator of, say, the quality of shots taken as Stoke have actually scored 4 more goals than QPR so far this season, despite having taken only 206 shots compared to QPR’s 278.

Total Shots on Target

The table of shots on target improves the correlation with the actual league table slightly:

Total Shots On Target TableLiverpool, #1 in the total shots table, slip to 5th and the top 7 would seem quite reasonable to the casual observer (perhaps in a different order). However, Newcastle outperform their league position again by 7 places and we have a similar ‘anomaly’ between QPR and Stoke as before. Perhaps Newcastle and QPR have been unlucky and Stoke/WBA lucky? Well, of course we can’t seriously infer that as we are not incorporating any form of defensive strength into this table – Stoke, for example, have conceded fewer goals at home than any other team. This leads to the next table:

Total Shots On Target For – Against

This is a shots on target difference table, to show the difference between average shots on target for minus shots on target conceded for each team:

Total Shots On Target Difference TableNote that the range and standard deviation of our difference to the actual league table has dropped moderately, suggesting that this table provides the closest indication of league table performance so far. Interestingly, on average, teams below 7th place all concede more shots on target than they have made. Again there are outliers: Newcastle stubbornly insist on taking 8th place throughout these tables, despite their league table position of 15th. The missing ingredient to make the leap to the actual league table is of course the goals scored and conceded themselves. So this table suggests that the teams who are better off in the actual league table have:

  1. Created more high quality shots
  2. Been more clinical at converting their shots
  3. Stifled the opposition attack into having worse shots
  4. Relied on good goalkeeping to save shots on target

For example, Manchester City and Manchester United have both had 68 big chances this season. United have scored 30, whilst City have scored 25. This shows United’s slight edge over City so far this season in terms of clinicism. When it comes to big chances conceded, the opposition have converted 10 big chances against United compared to 9 against City. So the overall goal difference just from big chances is +4 in United’s favour, a small number which nonetheless remains significant after 21 games.

Goal Difference Table

The last table in the post considers goal difference:

GD Table

From the goal difference table we see the impact of the 8-0 loss to Aston Villa’s position. Otherwise the table broadly tells the story of the league table itself which in itself is quite unsurprising, give or take the odd shuffle here and there.